The WeGov project analyzes social media data to help policymakers understand public opinions. It provides three types of analysis: topic analysis to identify discussion topics, influence analysis to find influential posts and users, and behavioral analysis to classify users. The WeGov website allows searching social networks and viewing analysis results through widgets that dynamically update. It aims to integrate social media analysis tools to help policymakers incorporate citizen voices into their work.
This document analyzes 653 tweets containing the words "public relations" or the acronym "PR" to understand how Twitter contributes to the development of public relations theory and practice. The tweets were categorized and the following key findings were reported:
1. The most common categories were announcements/events (28.6%) and discussions between users (18.7%), showing Twitter's role in networking and sharing information.
2. Press releases (4.3%) and jobs (15.2%) were also frequently discussed, demonstrating Twitter's use for professional purposes in PR.
3. Unexpectedly, few tweets discussed academic topics (2.3%), suggesting PR scholars do not often use Twitter, though it
This document provides a high-level and low-level description of a sentiment analysis system. At the high level, it collects text data, splits it into sentences, assigns polarity, checks for repeated words, and extracts sentiment. The low-level description details how it collects data from Facebook using APIs, processes the data by tagging parts of speech, analyzes polarity vs neutral sets, lists features, and builds a classifier using naive Bayes and dependencies between n-grams and parts of speech. The system aims to analyze sentiment from social media texts at both the document and sentence level.
The document discusses how social browsing and information filtering works on social media sites like Digg and Flickr. It finds that on Digg, users are more likely to vote for stories submitted by friends and stories that their friends have voted for. On Flickr, users put significant effort into sharing photos with groups, and photos are more likely to receive comments from the uploader's social connections than strangers. Social networks and browsing the activities of connections helps drive promotion and discovery of content on these social media sites.
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
1) The document proposes an enhanced technique to recommend communities to users in social networks based on the user's interests and their strong friends.
2) It identifies a user's area of interest by analyzing their posts and classifying keywords. It then determines the user's strong friends based on an enhanced quasi-clique technique, considering interaction strength.
3) Communities are recommended by considering both the user's interests and strong friends. This provides a more precise recommendation than only considering strong friends.
A topology based approach twittersdlfkjsdlkfjKunal Mittal
This document presents a topology-based approach for recommending followees (users to follow) on Twitter. The algorithm explores the graph of connections starting from a target user, selects candidate followees, and ranks them based on factors like the number of followers, number of common friends with the target user, and how often the candidate appears in the network. The approach was evaluated in an experiment with 14 real Twitter users to test how well it identified potentially interesting users to follow. Results showed the algorithm's potential for followee recommendation on Twitter by exploiting the social network structure rather than tweet content.
- The study analyzed over 43,000 ratings of tweets collected through a website that had users rate tweets in exchange for receiving feedback on their own tweets.
- They found that 36% of rated tweets were considered worth reading, 25% were not worth reading, and 39% were neutral. This suggests that users tolerate a large amount of less desirable content in their feeds.
- Through regression analysis, they determined that tweets sharing information, asking questions of followers, and self-promotion links were most valued, while presence maintenance updates, conversations, and personal status updates were less valued.
Tumblr 2014 - statistical overview and comparison with popular social servicesStephan Tschierschwitz
What is Tumblr: A Statistical Overview and Comparison with other popular social services, including blogosphere,
Twitter and Facebook, in answering a couple of key
questions: What is Tumblr? How is Tumblr different from
other social media networks?
Twitter: Social Network Or News Medium?Serge Beckers
This document analyzes Twitter as a social network and news media by studying its topological characteristics and information diffusion. The authors:
1) Crawled over 41 million user profiles, 1.47 billion social connections, and 106 million tweets to analyze Twitter's structure and behavior.
2) Found that Twitter has a non-power law distribution of followers, short paths of separation between users, and low reciprocity - distinguishing it from other social networks.
3) Ranked users by followers, PageRank and retweets, finding influence inferred from followers differs from popularity of tweets.
4) Analyzed trending topics and found most are news headlines that persist for days with participation from many users.
This document analyzes 653 tweets containing the words "public relations" or the acronym "PR" to understand how Twitter contributes to the development of public relations theory and practice. The tweets were categorized and the following key findings were reported:
1. The most common categories were announcements/events (28.6%) and discussions between users (18.7%), showing Twitter's role in networking and sharing information.
2. Press releases (4.3%) and jobs (15.2%) were also frequently discussed, demonstrating Twitter's use for professional purposes in PR.
3. Unexpectedly, few tweets discussed academic topics (2.3%), suggesting PR scholars do not often use Twitter, though it
This document provides a high-level and low-level description of a sentiment analysis system. At the high level, it collects text data, splits it into sentences, assigns polarity, checks for repeated words, and extracts sentiment. The low-level description details how it collects data from Facebook using APIs, processes the data by tagging parts of speech, analyzes polarity vs neutral sets, lists features, and builds a classifier using naive Bayes and dependencies between n-grams and parts of speech. The system aims to analyze sentiment from social media texts at both the document and sentence level.
The document discusses how social browsing and information filtering works on social media sites like Digg and Flickr. It finds that on Digg, users are more likely to vote for stories submitted by friends and stories that their friends have voted for. On Flickr, users put significant effort into sharing photos with groups, and photos are more likely to receive comments from the uploader's social connections than strangers. Social networks and browsing the activities of connections helps drive promotion and discovery of content on these social media sites.
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
1) The document proposes an enhanced technique to recommend communities to users in social networks based on the user's interests and their strong friends.
2) It identifies a user's area of interest by analyzing their posts and classifying keywords. It then determines the user's strong friends based on an enhanced quasi-clique technique, considering interaction strength.
3) Communities are recommended by considering both the user's interests and strong friends. This provides a more precise recommendation than only considering strong friends.
A topology based approach twittersdlfkjsdlkfjKunal Mittal
This document presents a topology-based approach for recommending followees (users to follow) on Twitter. The algorithm explores the graph of connections starting from a target user, selects candidate followees, and ranks them based on factors like the number of followers, number of common friends with the target user, and how often the candidate appears in the network. The approach was evaluated in an experiment with 14 real Twitter users to test how well it identified potentially interesting users to follow. Results showed the algorithm's potential for followee recommendation on Twitter by exploiting the social network structure rather than tweet content.
- The study analyzed over 43,000 ratings of tweets collected through a website that had users rate tweets in exchange for receiving feedback on their own tweets.
- They found that 36% of rated tweets were considered worth reading, 25% were not worth reading, and 39% were neutral. This suggests that users tolerate a large amount of less desirable content in their feeds.
- Through regression analysis, they determined that tweets sharing information, asking questions of followers, and self-promotion links were most valued, while presence maintenance updates, conversations, and personal status updates were less valued.
Tumblr 2014 - statistical overview and comparison with popular social servicesStephan Tschierschwitz
What is Tumblr: A Statistical Overview and Comparison with other popular social services, including blogosphere,
Twitter and Facebook, in answering a couple of key
questions: What is Tumblr? How is Tumblr different from
other social media networks?
Twitter: Social Network Or News Medium?Serge Beckers
This document analyzes Twitter as a social network and news media by studying its topological characteristics and information diffusion. The authors:
1) Crawled over 41 million user profiles, 1.47 billion social connections, and 106 million tweets to analyze Twitter's structure and behavior.
2) Found that Twitter has a non-power law distribution of followers, short paths of separation between users, and low reciprocity - distinguishing it from other social networks.
3) Ranked users by followers, PageRank and retweets, finding influence inferred from followers differs from popularity of tweets.
4) Analyzed trending topics and found most are news headlines that persist for days with participation from many users.
Detecting Spam Tags Against Collaborative Unfair Through Trust ModellingIOSR Journals
This document discusses methods for detecting spam tags in collaborative tagging systems through trust modeling. It classifies existing approaches into content trust modeling and user trust modeling. Content trust modeling assigns trust scores to content based on tags and users associated with it, while user trust modeling assigns trust scores to users based on their tagging behavior. The document also discusses challenges like evaluating models on multilingual data and lack of publicly available datasets for comparison. It concludes that trust modeling is important for enhancing reliability of social networks and content sharing services.
Social media for PR Communications - Success measurement planJose Sanchez
This document provides guidance on measuring the success of a social media campaign after its launch. It begins by defining social media measurement as the objective tracking, monitoring, collection, measurement and analysis of quantitative and qualitative data generated by participants to optimize social media tools, tactics and services. Key terms are defined, such as likes, shares, clicks and followers. The benefits of social media measurement are outlined, including optimizing campaigns. Methods of measurement are described, like setting goals, choosing metrics, using monitoring tools and dashboards, and ongoing optimization. Costs, reliability and examples of success are also discussed.
The document summarizes a research study that examined how the background knowledge of audiences on Twitter can help analyze the semantics of messages in Twitter streams. The researchers collected data from different Twitter streams over time, selected audiences for the streams, and estimated the background knowledge of audiences in different ways. They then evaluated how well the background knowledge helped predict hashtags of future tweets. The results showed the audience of a stream can provide useful knowledge, and streams with stable, interconnected communities tended to have more useful audiences.
18th home blog_twitter_English (12OCT2010) Han Woo PARK
This study analyzed the social network structures of Korean politicians on their homepages, blogs, and Twitter accounts between 2009-2010. It found that Twitter networks had higher connectivity and more evenly distributed links than homepage and blog networks. While politicians' homepage and blog networks mainly linked to others within their own party, Twitter networks showed more cross-party linking, especially between the two largest parties. The number of followers, followings, and tweets on Twitter in 2009 and 2010 were also found to be correlated. However, having more followers in 2009 did not guarantee more in 2010, showing the importance of continued online engagement.
The document analyzes Twitter data from 250,840 U.S. users who disclose their religious affiliations. It finds moderate correlations between the Twitter data and survey results on the distribution of religious groups across states and within states. Classifiers can accurately identify religious affiliations based on Twitter content and connections, with network features performing better. The analysis also shows strong assortativity, with users much more likely to connect to others of the same religion. However, the study only includes users who publicly declare their religion.
Research of usability of Mashup Tools done for Kent County Council as part of the Pic and Mix Pilot (2009), opening up Kent related datasets for all to use and exploit.
The document summarizes a research paper that proposes a personalized recommendation approach combining social network factors like interpersonal interest similarity and interpersonal rating behavior similarity. It uses probabilistic matrix factorization to predict ratings by considering these social network factors. The approach is evaluated on two large real-world social rating datasets and shows improved performance over approaches that only use social network information.
This document discusses mining social data from graphs by extracting the data from social networks using their APIs or by crawling websites. It describes representing the social data as graphs and using graph mining techniques like finding frequent patterns and substructures using algorithms like Apriori, pattern growth, and CL-CBI (ChunkingLess - Constraint-Based Induction). Decision trees can also be used to iteratively find patterns that branch the data. The challenges include the graph nature of the data, errors and unknowns, and vanity metrics, but graphs are useful for capturing complex social structures.
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
This document discusses community analysis in social media mining. It defines social media communities as groups of users who form links and interact based on common interests. Community detection aims to discover these implicit communities through algorithms. Member-based detection examines node characteristics like degree and similarity, while group-based detection finds communities with properties like being balanced, robust, modular, dense, or hierarchical. Analyzing communities provides insight into user interactions and behaviors that are only observable at a group level.
This document discusses how social media can be used for consumer insights in marketing research. It describes what types of data can be monitored on social media, including conversations, photos, videos and more. There are two main types of marketing research using social media: primary research involving direct data collection, and secondary research using existing internal or public data. Qualitative and quantitative research methods for analyzing social media data are also outlined. The document cautions about potential errors and biases when conducting social media research.
Friendbook: A Semantic-Based Friend Recommendation System for Social Networks1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Anticipating Discussion Activity on Community ForumsMatthew Rowe
This document presents a two-stage approach to predict discussion activity on online community forums. In the first stage, the approach identifies "seed posts" that are likely to generate replies through a classification model using user, content, and topic focus features. The second stage predicts the level of discussion generated by seed posts using regression models. Key findings include that posts with many URLs may reduce activity, while lower forum entropy and more complex language can increase activity. The approach achieved good performance in identifying seed posts and predicting discussion levels. Future work aims to apply the approach to other social media platforms.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
The document discusses a study that examines the correlation between actor centrality in social networks and their ability to coordinate projects. It outlines the research framework, which involves extracting coordination-related phrases from emails, calculating coordination scores bounded by project scopes, constructing social network matrices using centrality measures, and testing the association between centrality and coordination. Preliminary results on the Enron email network from 1997-2002 are presented. The methodology involves text mining the Enron dataset to calculate coordination scores and social network centrality metrics like degree, closeness, and betweenness centrality.
Groundhog day: near duplicate detection on twitterDan Nguyen
This document presents a framework for detecting near-duplicate tweets on Twitter. The framework analyzes tweet pairs using three approaches: (1) comparing syntactic characteristics like word overlap, (2) measuring semantic similarity, and (3) analyzing contextual information. Machine learning is used to learn patterns that help identify duplicate tweets. The framework is integrated into a Twitter search engine called Twinder to diversify search results and improve search quality. Extensive experiments evaluate strategies for detecting duplicate tweets and analyzing features that impact detection. The results show semantic features can boost duplicate detection performance.
The document discusses how recommender systems are evolving with the rise of Web 2.0 and social media platforms. It outlines how these new platforms provide more user data like demographics, social connections, tags, and folksonomies that can be used to develop new and improved recommendation algorithms. Specifically, it discusses how trust between users and exploiting the social graph can help with challenges like cold starts and attacks. It also examines using tags for content-based and collaborative filtering recommendations. Overall, the integration of social media data and semantic approaches is leading to more personalized and higher quality recommendations.
Das Ziel des WeGov-Projektes ist die Vernetzung von Bürgern mit der Politik durch soziale Netzwerke wie Twitter und Facebook. Dieses PDF beschreibt den aktuellen Projektstand durch den kürzlich veröffentlichten Software-Prototypen.
The WeGov project aims to improve engagement between governments and citizens by utilizing popular social networking sites like Twitter and Facebook. The project objectives are to develop a software solution that allows policymakers to use social media to engage citizens and understand their opinions to influence policy decisions. The WeGov toolbox includes tools to seed discussions, track opinions, and analyze social network discussions, topics, activities, and user behavior to provide insights for policymakers. The toolbox is intended to provide a flexible solution and enable governments to make better use of existing social media discussions.
Granite Telecommunications is a telecommunications company that specializes in providing flexible middleware solutions and services to multi-location businesses. They have over 800 employees, 15,000 clients with 175,000 locations nationwide, and manage over 1 million business lines. Granite offers consolidated invoicing, inventory management, and reporting through their web-based platform. Their services include local and long distance telephone, internet, data, voice, and structured cabling solutions.
Detecting Spam Tags Against Collaborative Unfair Through Trust ModellingIOSR Journals
This document discusses methods for detecting spam tags in collaborative tagging systems through trust modeling. It classifies existing approaches into content trust modeling and user trust modeling. Content trust modeling assigns trust scores to content based on tags and users associated with it, while user trust modeling assigns trust scores to users based on their tagging behavior. The document also discusses challenges like evaluating models on multilingual data and lack of publicly available datasets for comparison. It concludes that trust modeling is important for enhancing reliability of social networks and content sharing services.
Social media for PR Communications - Success measurement planJose Sanchez
This document provides guidance on measuring the success of a social media campaign after its launch. It begins by defining social media measurement as the objective tracking, monitoring, collection, measurement and analysis of quantitative and qualitative data generated by participants to optimize social media tools, tactics and services. Key terms are defined, such as likes, shares, clicks and followers. The benefits of social media measurement are outlined, including optimizing campaigns. Methods of measurement are described, like setting goals, choosing metrics, using monitoring tools and dashboards, and ongoing optimization. Costs, reliability and examples of success are also discussed.
The document summarizes a research study that examined how the background knowledge of audiences on Twitter can help analyze the semantics of messages in Twitter streams. The researchers collected data from different Twitter streams over time, selected audiences for the streams, and estimated the background knowledge of audiences in different ways. They then evaluated how well the background knowledge helped predict hashtags of future tweets. The results showed the audience of a stream can provide useful knowledge, and streams with stable, interconnected communities tended to have more useful audiences.
18th home blog_twitter_English (12OCT2010) Han Woo PARK
This study analyzed the social network structures of Korean politicians on their homepages, blogs, and Twitter accounts between 2009-2010. It found that Twitter networks had higher connectivity and more evenly distributed links than homepage and blog networks. While politicians' homepage and blog networks mainly linked to others within their own party, Twitter networks showed more cross-party linking, especially between the two largest parties. The number of followers, followings, and tweets on Twitter in 2009 and 2010 were also found to be correlated. However, having more followers in 2009 did not guarantee more in 2010, showing the importance of continued online engagement.
The document analyzes Twitter data from 250,840 U.S. users who disclose their religious affiliations. It finds moderate correlations between the Twitter data and survey results on the distribution of religious groups across states and within states. Classifiers can accurately identify religious affiliations based on Twitter content and connections, with network features performing better. The analysis also shows strong assortativity, with users much more likely to connect to others of the same religion. However, the study only includes users who publicly declare their religion.
Research of usability of Mashup Tools done for Kent County Council as part of the Pic and Mix Pilot (2009), opening up Kent related datasets for all to use and exploit.
The document summarizes a research paper that proposes a personalized recommendation approach combining social network factors like interpersonal interest similarity and interpersonal rating behavior similarity. It uses probabilistic matrix factorization to predict ratings by considering these social network factors. The approach is evaluated on two large real-world social rating datasets and shows improved performance over approaches that only use social network information.
This document discusses mining social data from graphs by extracting the data from social networks using their APIs or by crawling websites. It describes representing the social data as graphs and using graph mining techniques like finding frequent patterns and substructures using algorithms like Apriori, pattern growth, and CL-CBI (ChunkingLess - Constraint-Based Induction). Decision trees can also be used to iteratively find patterns that branch the data. The challenges include the graph nature of the data, errors and unknowns, and vanity metrics, but graphs are useful for capturing complex social structures.
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
This document discusses community analysis in social media mining. It defines social media communities as groups of users who form links and interact based on common interests. Community detection aims to discover these implicit communities through algorithms. Member-based detection examines node characteristics like degree and similarity, while group-based detection finds communities with properties like being balanced, robust, modular, dense, or hierarchical. Analyzing communities provides insight into user interactions and behaviors that are only observable at a group level.
This document discusses how social media can be used for consumer insights in marketing research. It describes what types of data can be monitored on social media, including conversations, photos, videos and more. There are two main types of marketing research using social media: primary research involving direct data collection, and secondary research using existing internal or public data. Qualitative and quantitative research methods for analyzing social media data are also outlined. The document cautions about potential errors and biases when conducting social media research.
Friendbook: A Semantic-Based Friend Recommendation System for Social Networks1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Anticipating Discussion Activity on Community ForumsMatthew Rowe
This document presents a two-stage approach to predict discussion activity on online community forums. In the first stage, the approach identifies "seed posts" that are likely to generate replies through a classification model using user, content, and topic focus features. The second stage predicts the level of discussion generated by seed posts using regression models. Key findings include that posts with many URLs may reduce activity, while lower forum entropy and more complex language can increase activity. The approach achieved good performance in identifying seed posts and predicting discussion levels. Future work aims to apply the approach to other social media platforms.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
The document discusses a study that examines the correlation between actor centrality in social networks and their ability to coordinate projects. It outlines the research framework, which involves extracting coordination-related phrases from emails, calculating coordination scores bounded by project scopes, constructing social network matrices using centrality measures, and testing the association between centrality and coordination. Preliminary results on the Enron email network from 1997-2002 are presented. The methodology involves text mining the Enron dataset to calculate coordination scores and social network centrality metrics like degree, closeness, and betweenness centrality.
Groundhog day: near duplicate detection on twitterDan Nguyen
This document presents a framework for detecting near-duplicate tweets on Twitter. The framework analyzes tweet pairs using three approaches: (1) comparing syntactic characteristics like word overlap, (2) measuring semantic similarity, and (3) analyzing contextual information. Machine learning is used to learn patterns that help identify duplicate tweets. The framework is integrated into a Twitter search engine called Twinder to diversify search results and improve search quality. Extensive experiments evaluate strategies for detecting duplicate tweets and analyzing features that impact detection. The results show semantic features can boost duplicate detection performance.
The document discusses how recommender systems are evolving with the rise of Web 2.0 and social media platforms. It outlines how these new platforms provide more user data like demographics, social connections, tags, and folksonomies that can be used to develop new and improved recommendation algorithms. Specifically, it discusses how trust between users and exploiting the social graph can help with challenges like cold starts and attacks. It also examines using tags for content-based and collaborative filtering recommendations. Overall, the integration of social media data and semantic approaches is leading to more personalized and higher quality recommendations.
Das Ziel des WeGov-Projektes ist die Vernetzung von Bürgern mit der Politik durch soziale Netzwerke wie Twitter und Facebook. Dieses PDF beschreibt den aktuellen Projektstand durch den kürzlich veröffentlichten Software-Prototypen.
The WeGov project aims to improve engagement between governments and citizens by utilizing popular social networking sites like Twitter and Facebook. The project objectives are to develop a software solution that allows policymakers to use social media to engage citizens and understand their opinions to influence policy decisions. The WeGov toolbox includes tools to seed discussions, track opinions, and analyze social network discussions, topics, activities, and user behavior to provide insights for policymakers. The toolbox is intended to provide a flexible solution and enable governments to make better use of existing social media discussions.
Granite Telecommunications is a telecommunications company that specializes in providing flexible middleware solutions and services to multi-location businesses. They have over 800 employees, 15,000 clients with 175,000 locations nationwide, and manage over 1 million business lines. Granite offers consolidated invoicing, inventory management, and reporting through their web-based platform. Their services include local and long distance telephone, internet, data, voice, and structured cabling solutions.
Projektvorstellung des EU-Forschungsprojekts WeGov im BundestagTimo Wandhoefer
WeGov - Where eGovernment meets the eSociety ist ein EU-Forschungsprojekt. Das Ziel ist - den digitalen Dialog zwischen Bürgern und Politik zu verbessern. Hierfür entwickelt WeGov einen Instrumentenkasten.
Die Präsentation zeigt den Halbzeitstand und dient der verstärkten Einbindung der Stakeholder in den Entwicklungsprozess.
This document outlines the types of questions that will be asked during structured interviews to evaluate Prototype 2.5 of the WeGov toolbox. The interviews will focus on understanding information and dissemination behavior on social media, views on the WeGov toolbox and its various features, use cases and best practices, and the future role of social networks in policymaking. Questions will explore priorities, functionality, usefulness, and opportunities for improving the toolbox.
Bringing Citizens’ Opinions to Members of ParliamentTimo Wandhoefer
This document outlines a strategy to identify where newspaper articles about policy statements are being discussed online. It proposes using automated Google searches of article headlines and URLs over time to track the most discussed locations. The results would help policymakers understand public opinions and participate in relevant online discussions.
ScholarLib: Sharing Resources and Data by linking scientific Information Port...Timo Wandhoefer
This document discusses the ScholarLib project, which aims to link scientific information portals with online social networks. The key features of ScholarLib would allow social network users to access metadata and datasets from information portals and share or annotate them. It also outlines prototypes built on XING, Facebook, and iversity to demonstrate linking author profiles between these sites and information portals. Future work discussed includes expanding to more social networks, evaluating the typology of networks, and integrating more databases and functionality.
Calum O'Connell created a magazine targeting new R&B artists. He structured the magazine like real publications with a front cover, table of contents, and double page spread. Feedback indicated the magazine clearly portrayed what a magazine focused on new artists would look like. The magazine represented up-and-coming musicians aged 16-25 through articles, photos, and trendy clothing styles. While most similar magazines like GQ focus on established artists, Calum's magazine spotlighted new music emerging online to attract a younger male audience interested in new music genres and artists. Calum conducted research online and used audience feedback to refine his magazine concept and address an unserved market.
The document discusses the concept of being "called" in the Bible. It provides several verses from the New Testament where the word "call" is used in reference to God summoning or inviting people to salvation. It also discusses the original Greek meanings of "a call" as getting someone's attention, a summons such as to court, or an invitation such as to a feast. The document emphasizes that the regular New Testament meaning of "call" refers to a religious summons or invitation from God.
This document summarizes several major corruption scandals in India that have occurred since the 1950s involving billions of dollars lost. It notes that India has an estimated $1.456 trillion in illegal funds stashed in offshore tax havens, more than any other country. Several massive scams are described in detail, including the 2G spectrum scandal that lost $40 billion, the Commonwealth Games fraud that lost $13 billion, and the Bofors scandal in the 1980s. The document argues that corruption poses an existential threat to India when the political system is plagued by plunder from the top levels of leadership down.
WeGov Analysis Tools to connect Policy Makers with Citizens OnlineTimo Wandhoefer
The document summarizes the WeGov project, which aims to connect policy makers with citizens online using analysis tools. The project involves partners from several European countries. It is developing a toolbox of social media analysis tools to help policy makers understand public opinions and engage citizens. The toolbox will allow searching social networks, analyzing discussions to identify topics and opinions, and modeling user behaviors. It is being tested with governments and is expected to be finalized in September 2012.
The document provides an overview of social media and mobile trends in Vietnam as of October 2012. Some key findings include:
- Vietnam's population is over 91.5 million people, with 69% living in rural areas.
- There are over 30.8 million internet users in Vietnam, representing a penetration rate of 34%.
- The most popular social networks are Facebook with 8.5 million users and Zing with 8.2 million users.
- Mobile penetration exceeds 100% with over 127 million subscribers.
Online Forums vs. Social Networks: Two Case Studies to support eGovernment wi...Timo Wandhoefer
This document summarizes two case studies on using topic opinion analysis to support eGovernment.
The first case study analyzed online forums to validate the accuracy of summarizing large amounts of data. It found the toolbox performed well on in-depth forums with nuanced discussions.
The second case study analyzed social media like Facebook and Twitter. It found the toolbox identified expected topics that stakeholders were already aware of. Topic quality was better for Twitter than Facebook.
Overall, the studies found topic analysis can help understand diverse communications but improvements like explaining algorithms are still needed. Qualitative validation was effective but time-consuming. Topic analysis shows potential to support politicians and organizations if combined with social media strategies.
The document discusses procrastination, why people procrastinate, and how to change procrastinating behaviors. It defines procrastination as putting things off due to laziness or carelessness. Common reasons for procrastinating include fear of failure or success, not prioritizing tasks, lack of knowledge to complete tasks, being too busy, enjoying procrastinating activities, and simply not wanting to do the task. Suggested ways to change include identifying why and how you procrastinate, creating a productive work environment without distractions, and avoiding myths like believing you work best under pressure.
FACES of Earl Haig Powerpoint (By Lilly Tong)ehfaces
The document outlines various programs and initiatives at Earl Haig Secondary School in 2010-2011 to promote diversity, equity, peace, and positive character attributes. These include posters, videos, fundraisers, workshops, and events around Peace Week, Attribute of the Month, anti-drug seminars, Equity Ambassador Program, and Day of Pink against homophobia. The goal is to celebrate differences, spread awareness, and create a safe and inclusive environment for students.
8 Powerful Social media marketing StrategiesJenny How
Here are 8 powerful social media strategies for businesses:
1. Meet customers where they are already online like Facebook, Twitter, and YouTube.
2. Begin conversations to set the tone of discussions about your business.
3. Build your reputation through social interactions to provide social proof and reassurance to new customers.
4. Keep up with competition by learning strategies that are working well for other companies.
The document discusses analyzing sentiment towards employee stock ownership plans (ESOP) on social media using sentiment analysis and clustering algorithms. It collects feedback on ESOP from four social monitoring tools - Social Mention, Trackur, Twendz, and Twitratr. It then uses K-means clustering, Expectation Maximization clustering, and VAR K-Means algorithms in the Tanagra1.4 data mining tool to cluster the results. The analysis finds that cluster 2 consistently indicates more negative sentiment than clusters 1 and 3, regardless of the clustering algorithm used.
Social media for PR - Communications - Success measurementJose Sanchez
This document provides guidance on measuring the success of social media campaigns through defining goals, key metrics, tracking tools, and ongoing optimization. Key steps include choosing metrics like followers, engagement, and sharing to track awareness, participation, and advocacy; using tools to monitor metrics and populate dashboards; and analyzing outcomes to see if goals were met and how the strategy can be improved. Measuring social media performance helps ensure it effectively meets communications objectives.
The WeGov project aims to improve engagement between government policy-makers and citizens by utilizing popular social media sites like Twitter and Facebook. The WeGov toolbox is a web application with tools to help policy-makers analyze social networks and discussions to better understand public opinions. It includes search, topic analysis, discussion activity analysis, and user behavior analysis functions. The goal is to provide a flexible solution that takes advantage of existing social media discussions to help policy-makers engage citizens and incorporate their views.
Social Media for Marketing: An Analysis of Digg.com Engagement and User BehaviorTyler Pace
In this report, we present an analysis of user engagement with social media via Digg.com. Viewer engagement was measured with OTOinsight’s Quantemo™ neuromarketing research system. Quantemo™ utilizes a multi-modal approach that combines self-report, physiological and neurological data to holistically and reliably measure user engagement with digital media. Analyzing the results from the Quantemo™ sources, we present a set of four insights concerning how users engage with social media and how the cues in social media systems positively inform user behavior.
This document discusses different approaches for analyzing social media data to gain customer insights:
1) Channel reporting tools provide overviews of specific social media platforms but lack deeper insights.
2) Scorecard systems aggregate data across sources but users cannot enhance the data.
3) Text mining analyzes sentiment but network analysis examines relationships; each technique has limitations alone.
4) The document proposes combining text mining, network analysis, and other techniques using a predictive analytics platform to generate new insights, as was done successfully for a major European telecom company.
It provides examples analyzing publicly available Slashdot data to identify influencers and show how sentiment relates to influence.
This document summarizes a research project on sentiment analysis of tweets about news. The researchers collected tweets related to news articles from various sources and analyzed the sentiment of the tweets to determine the overall public sentiment toward that news. They first preprocessed the tweet text through tokenization, removed stopwords, and calculated term frequencies. Next, they analyzed term co-occurrences to understand context. They also created visualizations of frequent terms. Finally, they used a naive Bayes classifier trained on labeled data to classify tweets in real-time as positive, negative, or neutral sentiment toward the news. The system aimed to provide a score indicating overall public sentiment toward each news article based on related tweets.
With the rise of social networking epoch, there has been a surge of user generated content. Micro blogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. We propose and investigate a paradigm to mine the sentiment from a popular real-time micro blogging service, Twitter, where users post real time reactions to and opinions about “everything”. In this paper, we expound a hybrid approach using both corpus based and dictionary based methods to determine the semantic orientation of the opinion words in tweets. A case study is presented to illustrate the use and effectiveness of the proposed system.
This document summarizes a research project on sentiment analysis of tweets about news. The researchers collected tweets related to news articles from various sources and analyzed the sentiment of the tweets to determine the overall public sentiment toward that news. They first preprocessed the tweet text through tokenization, removed stopwords, and calculated term frequencies. Next, they analyzed term co-occurrences to understand context. They also created visualizations of frequent terms. Finally, they used a naive Bayes classifier trained on labeled data to classify tweets in real-time as positive, negative, or neutral sentiment toward the news. The system aimed to provide a score indicating overall public sentiment toward each news article based on related tweets.
How to Analyze Your Conversations on Social MediaMohamed Mahdy
Conversation analysis is the process of analyzing conversations on social media surrounding topics like brands, products, or industries. There are four key categories to analyze: volume, influence, relevance, and sentiment. Volume looks at metrics like unique people, mentions, reach, and impressions. Influence identifies influential participants and content. Relevance determines how related conversations are. Sentiment gauges positive and negative reactions. Understanding conversations in these areas helps inform social media strategies.
Big Data Analytics- USE CASES SOLVED USING NETWORK ANALYSIS TECHNIQUES IN GEPHIRuchika Sharma
This report is done as a part in completion of our Big Data Analysis Course at Jindal Global Business School.
In this report, we have mainly focused on literature review of 10 use-cases in the visualization task. We have worked on use cases pertaining to varied use of social media site Twitter in the political, cultural and business context; use by drug marketers and musicians among others.
This document discusses metrics and analytics for social media. It defines metrics as counting and tracking past data, while analytics look at both past and present data strategically. Examples are provided of metrics like employee attrition rates versus analytics that examine the reasons behind those rates. Key frameworks for social media measurement are outlined, like return on investment, reach, likes, and cost of ignoring opportunities. Popular social media analytics tools from Google, Facebook, and Twitter are profiled. The document also discusses network analytics and using tools like NodeXL to map relationships and influence within social networks.
This document discusses metrics and analytics for social media. It defines metrics as counting and tracking past data, while analytics look at both past and present data strategically. Examples are provided of metrics like employee attrition rates versus analytics that examine the reasons behind those rates. Key frameworks for social media measurement are outlined, including types of engagement data from platforms like Facebook, Twitter, and Google Analytics. The document also covers principles of social network analysis and tools for mapping connections between users.
This document discusses metrics and analytics for social media. It defines metrics as counting and tracking past data, while analytics look at both past and present data strategically. Examples are provided of metrics like employee attrition rates versus analytics that examine the reasons behind those rates. Key frameworks for social media measurement are outlined, including types of engagement data from platforms like Facebook, Twitter, and Google Analytics. The document also covers principles of social network analysis for mapping influence through connections on platforms.
OTOinsights "An Analysis of Digg.com Engagement and User Behavior"One to One
This study presents an analysis of user engagement with social media via Digg.com. Understanding the behavior of Digg.com users will help marketers to better represent and promote their material on Digg.com as well as provide insights into the practices of social media users in general.
LiMoSINe Press kit introduces this project that integrates the studies of leading researchers over diverse topics with a view to enable new kinds of language-based technology search. Now we are developing 5 demonstrators: ORMA, ThemeStreams, FlickrDemo, DEESSE and Streamwatchr. http://limosine-project.eu/
We present Social Proxy, a SaaS platform that allows users to manage their Social Network accounts and to perform Trend and Sentiment Analysis on Social content.
The main services that the platform provides are:
- Social Accounts Management
- Monitoring
- Trend and Sentiment Analysis
- Analytics
- Campaign Management.
Social Proxy development started in 2010 and it is used in production since 2011. It has been used in Italy by important partners and customers, including NTT Data Italy, Coop Italia, Dolce & Gabbana, Benetton and Giunti Editore.
Some of the developments of Social Proxy were co-funded through the Regione Toscana research project SenTaClAus – http://sentaclaus.netseven.it
How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar
question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on
products and services which helps both the producers and consumers (stakeholders) to take effective and
efficient decision within a shortest period of time. Producers can have better knowledge of their products
and services through the sentiment analysis (ex. positive and negative comments or consumers likes and
dislikes) which will help them to know their products status (ex. product limitations or market status).
Consumers can have better knowledge of their interested products and services through the sentiment
analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know
their deserving products status (ex. product limitations or market status). For more specification of the
sentiment values, fuzzy logic could be introduced. Therefore, sentiment analysis with the help of fuzzy logic
(deals with reasoning and gives closer views to the exact sentiment values) will help the producers or
consumers or any interested person for taking the effective decision according to their product or service
interest.
How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar
question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on
products and services which helps both the producers and consumers (stakeholders) to take effective and
efficient decision within a shortest period of time. Producers can have better knowledge of their products
and services through the sentiment analysis (ex. positive and negative comments or consumers likes and
dislikes) which will help them to know their products status (ex. product limitations or market status).
Consumers can have better knowledge of their interested products and services through the sentiment
analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know
their deserving products status (ex. product limitations or market status). For more specification of the
sentiment values, fuzzy logic could be introduced. Therefore, sentiment analysis with the help of fuzzy logic
(deals with reasoning and gives closer views to the exact sentiment values) will help the producers or
consumers or any interested person for taking the effective decision according to their product or service
interest.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
This document summarizes a research paper on sentiment analysis using fuzzy logic. It discusses how sentiment analysis of consumer reviews can help both producers and consumers make effective decisions by understanding positive and negative feedback. It proposes using fuzzy logic to provide closer views of sentiment values. Key aspects covered include using the SentiWordNet lexical resource and analyzing tweets on Twitter to classify sentiments as positive, negative or neutral. The methodology searches for tweets on a topic, translates them to English, tags parts of speech, and analyzes sentiment values using SentiWordNet with fuzzy logic to help stakeholders make informed decisions.
This document summarizes a research paper on sentiment analysis using fuzzy logic. It discusses how sentiment analysis of consumer reviews can help both producers and consumers make effective decisions by understanding opinions on products and services. It also describes how fuzzy logic can be used to analyze sentiment values more precisely. The document outlines different approaches to sentiment analysis, including classifying reviews as positive or negative and determining the degree of sentiment. Twitter is presented as a source of consumer opinions that can be analyzed for sentiment using techniques like identifying emoticons, hashtags and targets.
Am 07. November fand im Landtag NRW das durch Leibniz organisierte Event Science meets Parliament statt. Hierbei bietet sich ein Austausch zwischen Wissenschaft und Politik. In diesem Rahmen wurden die vorliegenden Analyseergebnisse mit den Teilnehmern diskutiert und bewertet.
WeGov ist eine Software für die Politik zur Auswertung von Facebook und Twitter. Durch das Feedback aus Bundestag, Landtag NRW, Städten und Kommunen berücksichtigt WeGov gerade die Auswertung lokaler Bereiche. Die Präsentation enthält Beispiele mit Suchanfragen in NRW und Wuppertal. Das Event Offene Kommunen NRW wurde unter dem Twitter Hashtag #oknrw diskutiert. Die Auswertung hierzu findet sich auf den letzten beiden Slides.
Ziel dieser Evaluation ist es die aktuellen WeGov Analysekomponenten (Seite 3 ff.) sowie die Integration innerhalb der WeGov Toolbox durch Benutzer zu bewerten. Das gesamte Feedback fließt sowohl in den Prozess der Softwareentwicklung ein als auch in die Abschlussbeurteilung der Machbarkeitsstudie im Rahmen des Forschungsprojektes WeGov. WeGov befindet sich in der Endphase und wird mit Projektende im September die finale Software vorstellen.
WeGov Software Präsentation im Deutschen BundestagTimo Wandhoefer
WeGov entwickelt eine Software zur Vernetzung von Bürgern und der Politik im Internet. Dabei wird kein neues Beteiligungsportal entwickelt sondern auf vorhandene Soziale Online-Netzwerke aufgebaut.
Rethinking Governance via Social Networking: The case of direct vs. indirect ...Timo Wandhoefer
This document discusses two approaches to stakeholder engagement in governance via social networking: direct injection, where citizens directly engage with politicians on social media, and indirect injection, where citizens discuss policy issues online and their views indirectly influence politicians. It describes the WeGov project, an EU-funded initiative to build citizen-politician dialogue on existing social networks. Initial prototypes have been created and evaluated, with the goal of developing tools to support policymakers' work by analyzing online discussions.
WeGov - Where eGovernment meets the eSociety @ EGOV2011 conference - Overview...Timo Wandhoefer
This document provides an overview of the EU Project WeGov, which aims to enrich two-way dialogue between citizens and politics through social networking sites. The project extracts functionality from social networks to build a toolbox that supports policymakers' work and allows for citizen-politics dialogue through use cases. An initial prototype was demonstrated to the German Parliament and the toolbox is being evaluated before a final version is released in December 2011.
Digital Monitoring of societal Discussions in online Social NetworksTimo Wandhoefer
This document discusses a research project called WeGov that aims to improve dialogue between citizens and politics through social networks. The project monitors societal discussions on topics like nuclear power across multiple social media platforms. It has developed an initial toolbox of functions to analyze discussions and opinions. This includes identifying emotional comments, measuring activity levels, and clustering topics. The goal is to test these functions, get input from policymakers, and develop a demonstrator tool to help enrich two-way dialogue between citizens and government.
2. 2 What is WeGov?
WeGov – Where eGovernment meets the
eSociety - is an EU research project in the 7th
Research Framework Program (“ICT for
Governance and Policy Modeling”). For more
information, please visit our project website
http://wegov-project.eu.
Introduction
The WeGov project addresses the networking of citizens about politics, and with policy makers,
through social networks like Twitter and Facebook. It is not about investing in another citizen
participation platform. WeGov sees itself rather as a feasibility study to exploit the potential of social
networks for policy making, by having citizen opinions indirectly feed the decision-making processes.
The approach chosen consists in developing a site, including tools that support the political decision-
makers in the analysis of social networks. In terms of methodology, WeGov relies on the
participation of potential users (e.g. policy makers, communities, organizations) in the development
process of the software. The challenge is to reconcile as much as possible the requirements of these
user groups in terms of social media analysis with the technical feasibility of the WeGov analysis
models. WeGov has developed three alternative analytical approaches that are currently tested and
improved, as a basis for a later integration in the policy maker’s daily workflow.
Presentation of the WeGov analysis possibilities
Topic analysis
The topic analysis identifies groups of words that represent several areas of discussions that arise
within a wider debate.
This analysis is used for sorting of comments and users in the different concept groups and
can currently be used for Twitter and Facebook.
For each concept group approximately 3 user (key users) and about 3 comments (key posts)
are displayed.
Characteristic of key users and key posts is that they strongly refer to these topics, by
showing the highest overlap.
The analysis considers content related factors such as word frequency and the use of hash
tags (#). Not content related factors such as the number of retweets or likes are ignored in
this analysis.
Other social networks can also be analyzed using this model, but are currently not included.
In general, the quality of the concept groups should increase with the number and length of
comments.
The figures below show the topic analysis running on Twitter for the search term “European citizens’
initiative” on April 16. The analysis is also available for Facebook data.
3. Figure 1: Results of topic analysis
Basic properties for the analysis of the discussion activity and user
behavior
WeGov provides two main analyses in its Toolbox (explained below). The results of our research
indicate that different social networks behave in different ways, and the factors that make a post or a
user to be “important or relevant” in one social networks are probably not the same ones in another
social networks. The models that are currently integrated in the WeGov toolkit are those ones
trained with Twitter data.
To generate these models we collect a big amount of data (representative sample of the social
networks) and perform feature engineering over the data to extract those features that represent
the user and the content.
The model is optimized for English text. As a further development, the model will in addition be
adapted Facebook and will be optimized for the German language. In general, the following
properties are used for the behavior analysis, based on User features and on Content features:
User behavior on social networks
within social networks different levels of use can be distinguished. Consider, for example, a single
Twitter user, who usually follows other Twitter users and is also followed by Twitter users. These
dependencies and other properties are taken into account to determine the influence power of users
and their posts within Social Networks.
In-degree refers to the direction of the followers, i.e. the circle of persons who potentially
read a post of that particular user
Out-degree, on the other hand, refers to the Twitter users that a person will follow and
potentially read their posts and react by responding or re-tweeting.
User Participation
4. 4 What is WeGov?
Number of posts
Since when using social media
Frequency of Posts
General characteristics of the Posts
Length of Posts
Recommendation of the post by a third party (such as re-tweet)
Time of publication
Content characteristics of posts
Complexity is a measure for determining the level of content with respect to the word
accumulation - the higher the value, the greater the information content.
Readability is an index which describes to which extent a message is easy to understand – if
a message is understood from reading for the first time it is assumed to have good
readability.
Novelty value is a measure to determine the average number of times terms are occurring in
Posts - terms which appear for the first time increase the novelty value
Polarity measures the "mood" of the post and makes a statement how strongly the post
deviates from the average - this determines whether a post is particularly negatively
motivated
Timescale of the discussion activity
Figure 2: Graphical presentation of the
analyzed tweets in a timeframe
For the time being, WeGov performs the analysis on a download of the 99 last tweets, or all tweets
over a maximum period of one week if the total number of collected tweets is lower than 99. WeGov
shows how the analyzed tweets are concentrated in the concerned time period. In figure 2 above,
this means for instance that the search term “European citizens initiative” entered on April 16
generated 62 posts over the last week, with activity peaking on the first day.
5. 5 What is WeGov?
Results of analysis of the discussion activity
The purpose of this analysis is to predict which posts are going to generate more attention. The
results of our analysis indicate that in order to generate attention the content of the post is more
important than the reputation of the user within the SNS. In particular, those posts that generate
high levels of attention generally fit the following characteristics:
They were not written in the afternoon
They are written in a familiar language (the readability is high and the information content is
rather low)
They were written by people who follow many users and even read the news (high out-
degree)
The statement tends to be rather negative (stronger negative polarity)
In the WeGov toolkit the output of this analysis is translated in top posts to watch. The top users to
watch are computed by adding the scores of the top posts for each user. I.e., the top users are those
who generate more top posts (post that are likely to generate higher levels of attention).
For the search term “ACTA” this generated the following result on April 13.
Figure 3: Top 5 posts and Top 5 users to
watch
Results of analysis of user behavior
The purpose of this analysis is to classify users according to their behavior and interactions within the
SNS. For this analysis we only use user features, in-degree, out-degree and the properties of user
participation in the information (number of posts, length of use, and frequency of posts).
Within WeGov the following groups are considered:
6. Broadcaster is someone who posts with information and not in the possibility to
high daily rate and has a very high discuss
following (in-degree). However he Rare Poster is a user with very low post
follows very few people (out-degree). rate.
Information Source is someone who
posts a lot, is followed a lot but follows
more people than the Broadcaster His
involvement in social networks is
generally much higher than the
broadcaster.
Daily User is an average user in relation
to the number of posts, followers and
the people he follows himself.
Information Seeker is someone who
posts very rarely but follows a lot of
people. An information seeker is
generally interested in getting
Figure 4: User roles distribution
According to this behavioral characteristics, the
most influential and engaged role is the
Information Source, which is probably the people
that PM should pay more attention to.
Figure 5: Identification of users
7. 7 What is WeGov?
Presentation of the WeGov Website
The different analysis possibilities explained above can be used on the search page and can be
restricted to geographical areas of social networks.
On the home page, the analysis capabilities also function within small windows (widgets). The
widgets offer the advantage that they are immediately visible from login to the site and they display
updates of the analysis results of previously set search criteria.
WeGov – Application
The WeGov Website can be reached at
https://wegov.it-innovation.soton.ac.uk/. The website
is currently optimized for Firefox and Chrome. The
URL leads you to the screen shown under Figure 6.
Here you can login with your personal user name and
password. The site is currently available with an
English and German interface. We ask you to
apologize for editorial gaps, since this project is still
under development.
Figure 6: Login
WeGov – Home page
After successful registration, you will see the WeGov Home page (Home). The small windows
(widgets) show different results for Twitter and Facebook requests. Currently, a total of 100 Twitter
tweets and of 1700 Facebook posts can be polled. This figure is a technical limitation of the social
networks; we are working on improving this. To query Twitter data, no registration is required. For
Facebook, this does not apply. If you want to use the Facebook analysis, it is necessary that you are
logged in with a Facebook account. It is enough if you have opened Facebook within your browser
and you have logged in. WeGov will automatically detect the connection to Facebook and use this to
query data from Facebook. WeGov guarantees at this point that no personal data are stored or
retrieved.
You will find a total of 4 different types of widgets on the front page. Using the symbol "I" in white
on a gray circle, you can adjust the settings of the window, e.g. change the search word or duplicate
the window in order to compare results. If you delete the window, it cannot be recovered. But you
have the option to "hide" the selected window on the home page (consult the tab WeGov - Personal
Settings).
8. 8 What is WeGov?
Widget: Google Maps
In this widget (Figure 7) your current position is determined automatically, so you can automatically
display search results relative to your current location. When you are in Brussels, Brussels will be
pointed here.
Figure 7: Current location
If you are interested in analysis results related to other locations than your current location, you can
use the following widget (Figure 8) to enter and select other locations on the search page. To do this,
click on "Add new" and enter a location or region.
Figure 8: Store locations
9. 9 What is WeGov?
Widget: Topic Analysis
The following window (Figure 9) shows topics that are currently discussed on Twitter on Klaus
Wowereit. For each group, the term "key user" is displayed.
Figure 9: Topics discussed on a search term
The next window (Figure 10) uses the same analysis as Figure 4. All tweets are analyzed on content
to identify the topics currently under discussion on Klaus Wowereit. The difference is that here is a
geographical restriction of tweets has been on the current location (here Berlin)
Figure 10: Topics discussed locally on a search term
10. 10 What is WeGov?
The widget Facebook Post for: Angela Merkel1 (Figure 11) shows the most recent posts on the
Facebook Fan Page of Angela Merkel. In WeGov a total of last 25 posts is available. By choosing the
title detail page all 25 posts will be displayed in full length. In addition, the number of Facebook likes,
and the number of comments are visible. Below the post the unique Facebook-key (a combination) is
displayed. This can be entered in a separate window to start an analysis of the comments of this
post. We are currently working on a solution in which posts can be directly selected for analysis.
Using the icon (bottom right corner) another fan page to be entered for analysis.
Figure 11: Recent posts of the selected Facebook fan page
The widget in Figure 12 shows some of the topics discussed on the Facebook post 60 years
Protestant work... For each concept group, the term "key user" is displayed.
Figure 12: Topics discussed on a Facebook post
1
URL: https://www.facebook.com/AngelaMerkel (Searched in March 2012)
11. 11 What is WeGov?
The window in Figure 13 also refers to the post 60 years Protestant work ... Relevant comments are
displayed here. The detail view of the list is available by clicking on the title bar.
Figure 13: Comments on a selected Facebook post
Figure 14 shows the analysis result of the recent posts on the fan side, Angela Merkel. Topics and
"key users" are displayed.
Figure 14: Topics discussed on a selected Facebook fan page
12. 12 What is WeGov?
Widget: Behavior analysis
The following window (Figure 15) organizes tweets about the theme minimum wage according to the
user roles of the authors who publish the tweets. The group Information Source is considered to be
particularly interesting, because these users tend to have greater visibility in social networks (Twitter
in this case) than others.
Figure 15: user roles on a topic
Figure 16 shows a total of two Twitter user profiles with the role of Information Source. These users
are displayed as they were identified as a user with great influence from the set of tweets on the
subject of the theme minimum wage. This view displays users with other roles.
Figure 16: Users seldom posting on a topic
13. 13 What is WeGov?
Widget: Analysis results of external websites
The following windows are examples of the integration of non-WeGov services on the site. The idea
behind this is that there many good analytical capabilities exist in social media. WeGov wants to
integrate these analysis capabilities to its product range and provide them to its users and users. The
following window (Figure 17) integrates Twitter and displays the latest tweets about Klaus Wowereit
released near your current location (in this case Berlin).
Figure 17: Comments on a search term
Figure 18 also integrates analytical results from Twitter. In this case, Twitter-generated topics that
Twitter users are currently discussing in Germany are displayed.
Figure 18: Top 10 themes on Twitter
14. 14 What is WeGov?
WeGov - Personal settings
By clicking on the username in the upper right corner you can change personal settings as shown in
Figure 19. You can for instance set up a new password for your account. The list shows all My
widgets that are available on the homepage. If the box is checked, the window is displayed. If not, it
remains invisible.
Figure 19: User preferences
15. 15 What is WeGov?
WeGov - Search
Figure 20 shows the WeGov search page with detailed analysis of results. Currently 100 tweets from
Twitter are queried. This number will soon be expanded, as well as the ability to search on other
social networks. Enter a search word to start and possibly narrow down your search geographically
by selecting one location. The available locations are displayed on the map and must be pre-selected
in the window My Saved locations on the homepage. As results, you will see three lists. The first list
contains the hit list, which is generated through Twitter. The other two navigation points Topic
Analysis and Behavior Analysis show the previously described thematic sorting as well as the sorting
of the user and comments in relation to user behavior.
Figure 20: Detailed search
16. 16 What is WeGov?
Your contact persons for questions and constructive input
Dipl.-Inf. Timo Wandhöfer
GESIS – Leibniz-Institut für Sozialwissenschaften
Abteilung: Wissenstechnologien
Unter Sachsenhausen 6-8, 50667 Köln
Tel. +49 (0) 221 – 47694 – 544
E-Mail: timo.wandhoefer@gesis.org
Catherine van Eeckhaute
Deputy Director Gov2u
Tel + 32 (0)475-243522
E-Mail: catherine@gov2u.org