This document outlines an analysis of a social network extracted from Facebook data. It identifies key influential users through metrics like degree, betweenness, and closeness centrality. It also performs community detection based on user interests to identify groups like local businesses. Additionally, it performs geo-spatial analysis by grouping users by country to extract location-specific networks. Finally, it presents an engagement quadrant to categorize users based on influence and speed of information propagation. The overall analysis aims to identify target user groups for marketing and advertising strategies.
This document discusses social search engines, which take a user's social graph into account when returning search results. It provides three types of social searches: collective social search, which leverages trends from a network; friend-filtered social search, which shows what a user's friends have shared; and collaborative search, where users work together to find answers. The document also outlines benefits of social search like increased relevance and current results, and concludes that social search complements collaborative information seeking by allowing temporary collaboration during individual searches.
Evolving social data mining and affective analysis Athena Vakali
Evolving social data mining and affective analysis methodologies, framework and applications - Web 2.0 facts and social data
Social associations and all kinds of graphs
Evolving social data mining
Emotion-aware social data analysis
Frameworks and Applications
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
1) The document discusses how social media is impacting search engine optimization and how search engines are beginning to value social signals. It notes that links used to come from technical influencers but now come more from average users via social sharing.
2) It introduces the concept of a "share graph" that search engines are using to measure sharing activity across social networks and determine authority and trust.
3) It emphasizes that social media and search engine marketing can no longer be separate and that integration is key to success in getting found online.
Real-Time Marketing in a world of Search and SocialRob Garner
Presentation at Pubcon 2011 on the Navigating Social panel. Covers the changing landscape of real-time, search, and social, Google+, and what it means to means to real-time publishing and business.
This document discusses how game elements in social media platforms can influence the flow of information. It analyzes elements like user profiles and statistics on platforms like Twitter and Facebook. It maps player archetypes from games to user types on social networks and hypothesizes how different elements appeal to different types. For example, statistics may appeal more to "achiever" types while profiles appeal more to "socializers". The use of elements can encourage participation and influence how information spreads through rewarding users and facilitating connections between profiles.
Distributed Link Prediction in Large Scale Graphs using Apache SparkAnastasios Theodosiou
This document summarizes an approach to distributed link prediction in large graphs using Apache Spark. It discusses using machine learning techniques like locality sensitive hashing to predict links between nodes in a graph based on document similarity metrics and other structural features. The approach is tested on a graph of 27,770 academic papers linked by 352,857 citations. Both supervised and unsupervised machine learning methods are explored, including treating it as a binary classification problem and using locality sensitive hashing and MinHashLSH through Apache Spark to efficiently handle the large data volumes. The results suggest this distributed approach can accurately predict new links in large graphs.
The document summarizes the LinkedIn Sales Navigator premium social selling solution. It allows sales professionals to [1] quickly find and qualify leads by leveraging LinkedIn's vast network and profile data. Sales Navigator provides tools like Lead Builder to prioritize leads based on criteria and common connections. [2] It enables engagement with decision-makers directly through InMails, referencing shared connections to increase response rates. [3] Seamless integration with Dynamics CRM allows synchronization of profile data for a comprehensive view of leads and customers.
This document discusses social search engines, which take a user's social graph into account when returning search results. It provides three types of social searches: collective social search, which leverages trends from a network; friend-filtered social search, which shows what a user's friends have shared; and collaborative search, where users work together to find answers. The document also outlines benefits of social search like increased relevance and current results, and concludes that social search complements collaborative information seeking by allowing temporary collaboration during individual searches.
Evolving social data mining and affective analysis Athena Vakali
Evolving social data mining and affective analysis methodologies, framework and applications - Web 2.0 facts and social data
Social associations and all kinds of graphs
Evolving social data mining
Emotion-aware social data analysis
Frameworks and Applications
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
1) The document discusses how social media is impacting search engine optimization and how search engines are beginning to value social signals. It notes that links used to come from technical influencers but now come more from average users via social sharing.
2) It introduces the concept of a "share graph" that search engines are using to measure sharing activity across social networks and determine authority and trust.
3) It emphasizes that social media and search engine marketing can no longer be separate and that integration is key to success in getting found online.
Real-Time Marketing in a world of Search and SocialRob Garner
Presentation at Pubcon 2011 on the Navigating Social panel. Covers the changing landscape of real-time, search, and social, Google+, and what it means to means to real-time publishing and business.
This document discusses how game elements in social media platforms can influence the flow of information. It analyzes elements like user profiles and statistics on platforms like Twitter and Facebook. It maps player archetypes from games to user types on social networks and hypothesizes how different elements appeal to different types. For example, statistics may appeal more to "achiever" types while profiles appeal more to "socializers". The use of elements can encourage participation and influence how information spreads through rewarding users and facilitating connections between profiles.
Distributed Link Prediction in Large Scale Graphs using Apache SparkAnastasios Theodosiou
This document summarizes an approach to distributed link prediction in large graphs using Apache Spark. It discusses using machine learning techniques like locality sensitive hashing to predict links between nodes in a graph based on document similarity metrics and other structural features. The approach is tested on a graph of 27,770 academic papers linked by 352,857 citations. Both supervised and unsupervised machine learning methods are explored, including treating it as a binary classification problem and using locality sensitive hashing and MinHashLSH through Apache Spark to efficiently handle the large data volumes. The results suggest this distributed approach can accurately predict new links in large graphs.
The document summarizes the LinkedIn Sales Navigator premium social selling solution. It allows sales professionals to [1] quickly find and qualify leads by leveraging LinkedIn's vast network and profile data. Sales Navigator provides tools like Lead Builder to prioritize leads based on criteria and common connections. [2] It enables engagement with decision-makers directly through InMails, referencing shared connections to increase response rates. [3] Seamless integration with Dynamics CRM allows synchronization of profile data for a comprehensive view of leads and customers.
The document summarizes the LinkedIn Sales Navigator premium social selling solution. It allows sales professionals to [1] quickly find and qualify leads by leveraging LinkedIn's vast network and profile data. Sales Navigator provides tools like Lead Builder to prioritize leads based on criteria and common connections. [2] It enables engagement with decision-makers directly through InMails, referencing shared connections to increase response rates. [3] Sales Navigator seamlessly integrates with Microsoft Dynamics CRM to automatically match LinkedIn profiles to contact records and provide up-to-date insights on prospects.
The document summarizes LinkedIn Sales Navigator, a premium social selling solution that helps sales professionals find and qualify leads. Some key benefits include:
1) Leveraging LinkedIn's vast network of over 160 million members to discover new leads and gain insights into contacts and companies.
2) Prioritizing leads lists using features like Lead Builder that filter contacts based on criteria.
3) Directly engaging with decision-makers using InMail, having an average of 8 InMails generate a new sales opportunity.
4) Seamlessly integrating with Microsoft Dynamics CRM to automatically match LinkedIn profiles to contacts and leverage updated profile information.
LinkedIn Sales Navigator is a premium social selling solution that helps sales professionals:
1) Find and prioritize leads through insights from LinkedIn's vast network, tools to organize leads, and features to filter lists by criteria.
2) Engage decision-makers directly through personalized outreach like InMails, leveraging connections from their own network and coworkers' networks via Team Link.
3) Rely on trusted profile data for accurate insights on leads and discover influencers and common connections more quickly to build relationships.
LinkedIn Sales Navigator is a premium social selling solution that helps sales professionals:
1) Find and prioritize leads through insights from LinkedIn's vast network, tools to organize leads, and features to filter lists by criteria.
2) Engage decision-makers directly through personalized outreach like InMails, leveraging connections from their own network and coworkers' networks via Team Link.
3) Rely on trusted profile data for accurate insights on leads and discover influencers and common connections more quickly to build relationships.
This document discusses the shift from the traditional web to the social web, where users engage in more complex online interactions and activities. It introduces Davai's approach to predictive user modeling based on analyzing users' activity streams on social networks to generate models that can predict user demographics, behaviors and interests. Key areas of investment include online marketing, personalized content and context-aware mobile services, with all services being permission-based and providing value to users. Davai analyzes social network communication and uses machine learning on activity stream data to generate predictive user models that classify users into interest and response categories.
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Social CRM the new rules of relationship managementPlínio Okamoto
This document outlines 18 use cases for social customer relationship management (Social CRM). It begins by noting that customers have increasingly connected with each other through social media, while organizations have struggled to keep up. Social CRM can help reconnect organizations with customers by engaging them in social channels. The document identifies seven categories of Social CRM use cases and provides details on 18 specific use cases. It recommends starting with building social customer insights as the foundation for all Social CRM initiatives. This involves monitoring social media, mapping customer relationships, managing customer data, using middleware, and measuring outcomes.
Managed Security Services: 2018 Media & Influencer AnalysisZeno Group
The document analyzes media coverage and influencer activity related to managed security services (MSS) in 2018. It finds that ZDNet, CSO Online, and Threatpost drove the most narrative around MSS, while topics like data security, network security, and SOC monitoring resonated most. Notable influencers in the space included Joe Panettiere, Jovi Umawing, and Brian Krebs, who frequently referenced publications like ZDNet, BleepingComputer, and Threatpost. Trending topics among influencers were cloud security, data security, and network security.
Sales Navigator helps sales professionals build connections on LinkedIn, engage with decision-makers directly through InMail messages, and integrate with Salesforce CRM to prioritize leads and leverage coworker networks. It provides tools like Lead Builder, Team Link, profile and company insights to help salespeople identify prospects, qualify leads, and shorten sales cycles. Premium features in Sales Navigator offer expanded search capabilities, profile visibility, and CRM integration to leverage trusted LinkedIn data and drive more pipeline and revenue.
Sales Navigator helps sales professionals build connections on LinkedIn, prioritize leads using insights on titles, companies, and social proximity. It provides direct messaging capabilities to contact decision-makers along with seamless integration with Salesforce CRM. Premium features include visibility into third-degree connections, tracking profile views, and leveraging coworker networks through Team Link to facilitate introductions.
Sales Navigator helps sales professionals build connections on LinkedIn, engage with decision-makers directly through InMail messages, and integrate with Salesforce CRM to prioritize leads and leverage coworker networks to reach prospects. It provides tools like Lead Builder, Team Link, profile and company insights to efficiently grow business opportunities on the world's largest professional network. Premium features allow users to discover influencers in their extended network, see who viewed their profile, and integrate trusted LinkedIn data seamlessly into their sales process.
Blockchain: 2018 Media & Influencer AnalysisZeno Group
This document analyzes 2018 media and influencer coverage of blockchain technology. It finds that Forbes, Coindesk, and CCN published the most articles about blockchain, while topics around cryptocurrency, smart contracts, and AI resonated the most. Notable influencers in the blockchain space included Tom Stankewicz, Frank Chaparro, and Kjerstin Erickson, who primarily consumed and shared articles from Coindesk, CCN, and Bitcoinist. The analysis provides insights on how to leverage key media publications and influencers to help drive strategic communications around blockchain.
An effective on-site social optimization strategy consists of three key components: social connectivity, the connected experience, and social analytics. Social connectivity involves connecting a site to social networks like Facebook, Twitter, and LinkedIn using their APIs. This allows users to register using their social identities and share content back to their networks. The connected experience enhances how users can share and interact socially on the site. Social analytics analyzes the results to improve the social business strategy and see returns from socially-referred traffic.
Putting the Pieces Together: Finding Value in Unstructured DataSocial Media Today
The document discusses leveraging unstructured data from social media and customer interactions. It describes how companies can integrate this unstructured data with structured customer profile data to better understand customer needs and target promotions. It also discusses tools like Teradata and various social media analytics vendors that can help companies extract insights from large volumes of unstructured text data.
Research.ly by PeopleBrowsr - Next Generation Social SearchPeopleBrowsr
Research.ly by PeopleBrowsr is the site that changes everything. It's a Next Generation Social Search.
This slide deck shows how to use Research.ly to search and explore the collective consciousness of people around you and around the world.
PeopleBrowsr Keynote Slides - About UsPeopleBrowsr
PeopleBrowsr is a high-tech social analytics company that aims to index every public human conversation on social media to analyze trends, influencers, and conversations. It monitors over 30 terabytes of social data from platforms like Twitter, Facebook, and others. The company uses this data to help enterprises identify brand champions and influential communities to target effective marketing and engagement campaigns. It offers services like sentiment analysis, interest graph mapping, and customized reporting to track campaign metrics and conversations.
The document discusses best practices for using social networks to promote a business, including familiarizing yourself with the tools and terms of service, joining relevant groups to build readership, and building relationships over time by regularly posting engaging content within the rules of each network. While social network links currently may not be indexed by search engines, regularly posting quality content positions a business well for when search engines do begin recognizing these types of links.
Credibility and Influence - AdTech London 2011 - Jodee RichPeopleBrowsr
Jodee Rich discusses how social media is evolving and how marketers can leverage social influencers. Over the next two years, the "interest graph" will replace the "social graph" as influencers and authorities become more important. Case studies are presented showing how social data can replace Nielsen ratings and how identifying influential users helped campaigns increase engagement. A new concept called "Kred" is introduced to measure social influence and outreach in a transparent, community-based way.
View state and session state are two common ways to store information on the server side in ASP.NET applications. View state uses a hidden field to store information for a single page postback, while session state stores information on the server for an entire user session across multiple pages. Both allow storing simple data types as well as custom objects by making them serializable. View state data is sent back and forth with each request while session state remains on the server.
The document provides instructions for setting up and using the Tobii X60 and X120 eye trackers. It discusses connecting the eye tracker to a computer via an Ethernet connection, installing necessary software, and configuring network settings. It also provides guidelines for product care, safety information, and instructions for configuring the physical setup and parameters using the X120 Configuration Tool. Troubleshooting tips and specifications for the eye trackers are included in appendices.
He aquí las direcciones en Google Maps:
Direcciones: https://www.google.com/maps/place/Calle+34+%235C-25,+Chinchilla,+Valledupar,+Cesar/@10.4194289,-73.3514609,17z/data=!3m1!4b1!4m5!3m4!1s0x8ef78c6b5c7c4b5d:0x6c5d1d5d7c5c6b5c!8m2!3d10.4194289!4d-73.3492722
Canales: https://www.
The document summarizes the LinkedIn Sales Navigator premium social selling solution. It allows sales professionals to [1] quickly find and qualify leads by leveraging LinkedIn's vast network and profile data. Sales Navigator provides tools like Lead Builder to prioritize leads based on criteria and common connections. [2] It enables engagement with decision-makers directly through InMails, referencing shared connections to increase response rates. [3] Sales Navigator seamlessly integrates with Microsoft Dynamics CRM to automatically match LinkedIn profiles to contact records and provide up-to-date insights on prospects.
The document summarizes LinkedIn Sales Navigator, a premium social selling solution that helps sales professionals find and qualify leads. Some key benefits include:
1) Leveraging LinkedIn's vast network of over 160 million members to discover new leads and gain insights into contacts and companies.
2) Prioritizing leads lists using features like Lead Builder that filter contacts based on criteria.
3) Directly engaging with decision-makers using InMail, having an average of 8 InMails generate a new sales opportunity.
4) Seamlessly integrating with Microsoft Dynamics CRM to automatically match LinkedIn profiles to contacts and leverage updated profile information.
LinkedIn Sales Navigator is a premium social selling solution that helps sales professionals:
1) Find and prioritize leads through insights from LinkedIn's vast network, tools to organize leads, and features to filter lists by criteria.
2) Engage decision-makers directly through personalized outreach like InMails, leveraging connections from their own network and coworkers' networks via Team Link.
3) Rely on trusted profile data for accurate insights on leads and discover influencers and common connections more quickly to build relationships.
LinkedIn Sales Navigator is a premium social selling solution that helps sales professionals:
1) Find and prioritize leads through insights from LinkedIn's vast network, tools to organize leads, and features to filter lists by criteria.
2) Engage decision-makers directly through personalized outreach like InMails, leveraging connections from their own network and coworkers' networks via Team Link.
3) Rely on trusted profile data for accurate insights on leads and discover influencers and common connections more quickly to build relationships.
This document discusses the shift from the traditional web to the social web, where users engage in more complex online interactions and activities. It introduces Davai's approach to predictive user modeling based on analyzing users' activity streams on social networks to generate models that can predict user demographics, behaviors and interests. Key areas of investment include online marketing, personalized content and context-aware mobile services, with all services being permission-based and providing value to users. Davai analyzes social network communication and uses machine learning on activity stream data to generate predictive user models that classify users into interest and response categories.
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Social CRM the new rules of relationship managementPlínio Okamoto
This document outlines 18 use cases for social customer relationship management (Social CRM). It begins by noting that customers have increasingly connected with each other through social media, while organizations have struggled to keep up. Social CRM can help reconnect organizations with customers by engaging them in social channels. The document identifies seven categories of Social CRM use cases and provides details on 18 specific use cases. It recommends starting with building social customer insights as the foundation for all Social CRM initiatives. This involves monitoring social media, mapping customer relationships, managing customer data, using middleware, and measuring outcomes.
Managed Security Services: 2018 Media & Influencer AnalysisZeno Group
The document analyzes media coverage and influencer activity related to managed security services (MSS) in 2018. It finds that ZDNet, CSO Online, and Threatpost drove the most narrative around MSS, while topics like data security, network security, and SOC monitoring resonated most. Notable influencers in the space included Joe Panettiere, Jovi Umawing, and Brian Krebs, who frequently referenced publications like ZDNet, BleepingComputer, and Threatpost. Trending topics among influencers were cloud security, data security, and network security.
Sales Navigator helps sales professionals build connections on LinkedIn, engage with decision-makers directly through InMail messages, and integrate with Salesforce CRM to prioritize leads and leverage coworker networks. It provides tools like Lead Builder, Team Link, profile and company insights to help salespeople identify prospects, qualify leads, and shorten sales cycles. Premium features in Sales Navigator offer expanded search capabilities, profile visibility, and CRM integration to leverage trusted LinkedIn data and drive more pipeline and revenue.
Sales Navigator helps sales professionals build connections on LinkedIn, prioritize leads using insights on titles, companies, and social proximity. It provides direct messaging capabilities to contact decision-makers along with seamless integration with Salesforce CRM. Premium features include visibility into third-degree connections, tracking profile views, and leveraging coworker networks through Team Link to facilitate introductions.
Sales Navigator helps sales professionals build connections on LinkedIn, engage with decision-makers directly through InMail messages, and integrate with Salesforce CRM to prioritize leads and leverage coworker networks to reach prospects. It provides tools like Lead Builder, Team Link, profile and company insights to efficiently grow business opportunities on the world's largest professional network. Premium features allow users to discover influencers in their extended network, see who viewed their profile, and integrate trusted LinkedIn data seamlessly into their sales process.
Blockchain: 2018 Media & Influencer AnalysisZeno Group
This document analyzes 2018 media and influencer coverage of blockchain technology. It finds that Forbes, Coindesk, and CCN published the most articles about blockchain, while topics around cryptocurrency, smart contracts, and AI resonated the most. Notable influencers in the blockchain space included Tom Stankewicz, Frank Chaparro, and Kjerstin Erickson, who primarily consumed and shared articles from Coindesk, CCN, and Bitcoinist. The analysis provides insights on how to leverage key media publications and influencers to help drive strategic communications around blockchain.
An effective on-site social optimization strategy consists of three key components: social connectivity, the connected experience, and social analytics. Social connectivity involves connecting a site to social networks like Facebook, Twitter, and LinkedIn using their APIs. This allows users to register using their social identities and share content back to their networks. The connected experience enhances how users can share and interact socially on the site. Social analytics analyzes the results to improve the social business strategy and see returns from socially-referred traffic.
Putting the Pieces Together: Finding Value in Unstructured DataSocial Media Today
The document discusses leveraging unstructured data from social media and customer interactions. It describes how companies can integrate this unstructured data with structured customer profile data to better understand customer needs and target promotions. It also discusses tools like Teradata and various social media analytics vendors that can help companies extract insights from large volumes of unstructured text data.
Research.ly by PeopleBrowsr - Next Generation Social SearchPeopleBrowsr
Research.ly by PeopleBrowsr is the site that changes everything. It's a Next Generation Social Search.
This slide deck shows how to use Research.ly to search and explore the collective consciousness of people around you and around the world.
PeopleBrowsr Keynote Slides - About UsPeopleBrowsr
PeopleBrowsr is a high-tech social analytics company that aims to index every public human conversation on social media to analyze trends, influencers, and conversations. It monitors over 30 terabytes of social data from platforms like Twitter, Facebook, and others. The company uses this data to help enterprises identify brand champions and influential communities to target effective marketing and engagement campaigns. It offers services like sentiment analysis, interest graph mapping, and customized reporting to track campaign metrics and conversations.
The document discusses best practices for using social networks to promote a business, including familiarizing yourself with the tools and terms of service, joining relevant groups to build readership, and building relationships over time by regularly posting engaging content within the rules of each network. While social network links currently may not be indexed by search engines, regularly posting quality content positions a business well for when search engines do begin recognizing these types of links.
Credibility and Influence - AdTech London 2011 - Jodee RichPeopleBrowsr
Jodee Rich discusses how social media is evolving and how marketers can leverage social influencers. Over the next two years, the "interest graph" will replace the "social graph" as influencers and authorities become more important. Case studies are presented showing how social data can replace Nielsen ratings and how identifying influential users helped campaigns increase engagement. A new concept called "Kred" is introduced to measure social influence and outreach in a transparent, community-based way.
View state and session state are two common ways to store information on the server side in ASP.NET applications. View state uses a hidden field to store information for a single page postback, while session state stores information on the server for an entire user session across multiple pages. Both allow storing simple data types as well as custom objects by making them serializable. View state data is sent back and forth with each request while session state remains on the server.
The document provides instructions for setting up and using the Tobii X60 and X120 eye trackers. It discusses connecting the eye tracker to a computer via an Ethernet connection, installing necessary software, and configuring network settings. It also provides guidelines for product care, safety information, and instructions for configuring the physical setup and parameters using the X120 Configuration Tool. Troubleshooting tips and specifications for the eye trackers are included in appendices.
He aquí las direcciones en Google Maps:
Direcciones: https://www.google.com/maps/place/Calle+34+%235C-25,+Chinchilla,+Valledupar,+Cesar/@10.4194289,-73.3514609,17z/data=!3m1!4b1!4m5!3m4!1s0x8ef78c6b5c7c4b5d:0x6c5d1d5d7c5c6b5c!8m2!3d10.4194289!4d-73.3492722
Canales: https://www.
This document summarizes a seminar on social data and privacy. It discusses what social data is, where it comes from, and different types of social data like demographics, interests, actions, interactions, recency and frequency. Social data provides insights into customers, brands, and can be used to create relationships and graphs to better understand people. When modeled effectively at large scale, social data provides significant business value across marketing, sales, and other functions by enabling more precise targeting and a personalized customer experience.
Managing it security and data privacy securityAlpesh Doshi
This document discusses managing IT security and data privacy to enhance the customer experience. It notes that customers now expect a better relationship with brands, and that social media data has become a new currency for engagement. It outlines the types of personal data available on social media sites and discusses the need for financial organizations to implement data protection, privacy regulations, and risk standards to securely manage this data. Key challenges include the lack of integrated policies and monitoring across most financial organizations regarding social media data security and use. The document argues that new security solutions and architectures are required that incorporate security from the start to address these challenges and regulatory requirements while still enabling improved customer engagement.
Marketing analytics alpesh doshi social network analysis - using social gra...Alpesh Doshi
- Social network analysis uses social graph constructs to understand user behavior, recommendations, and influence. A social graph models relationships between connected social objects like people, interests, and actions. Characteristics of social graphs include strong and weak ties, centrality, degree, betweenness, and closeness. Social graphs can be used for recommendation engines, interest graphs, influence networks, sentiment analysis, and searching, scoring, and ranking. The use of social graphs in marketing is still nascent but will change how marketing is done in the future.
Structural Balance Theory Based Recommendation for Social Service PortalYogeshIJTSRD
There is enormous data present in our world. Therefore in order to access the most accurate information is becoming more difficult and complicated. As a result many relevant information gets missed which leads to much duplication of work and effort. Due to the huge search results, the user will generally have difficulty in identifying the relevant ones. To solve this problem, a recommendation system is used. A recommendation system is nothing but a filtering information system, which is used to predict the relevance of retrieved information according to the user’s needs for some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide huge records about any requested item or service. A proper recommendation system is used to separate this information result. A recommendation system can be improved further if supported with a level of trust information. That is, recommendations are prioritized according to their level of trust. Recommending appropriate needs social service to the target volunteers will become the key to ensure continuous success of social service. Today, many social service systems does not adopt any recommendation techniques. They provide advertisement or highlights request for a small commission. G. Banupriya | M. Anand "Structural Balance Theory-Based Recommendation for Social Service Portal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41216.pdf Paper URL: https://www.ijtsrd.comengineering/software-engineering/41216/structural-balance-theorybased-recommendation-for-social-service-portal/g-banupriya
Identical Users in Different Social Media Provides Uniform Network Structure ...IJMTST Journal
The primary point of this venture is secure the client login and information sharing among the interpersonal organizations like Gmail, Face book and furthermore find unknown client utilizing this systems. On the off chance that the first client not accessible in the systems, but rather their companions or mysterious client knows their login points of interest implies conceivable to abuse their talks. In this venture we need to defeat the mysterious client utilizing the system without unique client information. Unapproved client utilizing the login to talk, share pictures or recordings and so on. This is the issue to be overcome in this venture .That implies client initially enlist their subtle elements with one secured question and reply. Since the unknown client can erase their talk or information. In this by utilizing the secured questions we need to recuperate the unapproved client talk history or imparting subtle elements to their IP address or MAC address. So in this venture they have discovered an approach to keep the mysterious clients abuse the first client login points.
Startup Network Pitch. Reduce your transaction cost and boost new business de...Mario Scuderi
Startup Network (STN) provides a solution to analyze professional networks and measure key performance indicators. STN uses social network data and algorithms to quantify relationships and identify influential individuals. This helps reduce costs and find new opportunities by improving networking strategies. STN's metrics help investors, recruiters and entrepreneurs make better decisions and maximize the value of their networks.
NOVEL MACHINE LEARNING ALGORITHMS FOR CENTRALITY AND CLIQUES DETECTION IN YOU...ijaia
This document summarizes a research paper that used machine learning algorithms to analyze social networks on YouTube. The researchers used unsupervised learning techniques like clustering and centrality measures to identify communities and influential users. Specifically, they used Louvain modularity and spectral clustering to detect groups for advertising purposes. Degree centrality and clique centrality were calculated to find central nodes that could be targeted for sponsorship deals. The experiments showed the algorithms could successfully find tightly-knit groups and key influencers within the larger YouTube network.
NOVEL MACHINE LEARNING ALGORITHMS FOR CENTRALITY AND CLIQUES DETECTION IN YOU...gerogepatton
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
Novel Machine Learning Algorithms for Centrality and Cliques Detection in You...gerogepatton
This document summarizes a research paper that used machine learning algorithms to analyze social networks on YouTube. The researchers used unsupervised learning techniques like clustering and centrality measures to identify communities and influential users. Specifically, they used Louvain modularity and spectral clustering to detect groups for advertising purposes. Degree centrality and clique centrality were calculated to find central nodes that could be identified as influencers for product sponsorship. The experiments showed the algorithms could successfully find tightly-knit groups and key users within the larger YouTube network.
ATC full paper format-2014 Social Networks in Telecommunications Asoka Korale...Asoka Korale
This summarizes a document describing a novel approach to analyzing social networks in mobile telecommunications by modeling call patterns between subscribers. It identifies leaders and communities by processing call initiation and termination data. Communities are detected using influence diffusion algorithms. Results are presented from a corporate network analyzed, identifying leaders and communities formed around them. The identified leaders are validated using existing centrality measures. The approach allows estimating the degree to which individuals belong to multiple overlapping communities.
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.
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BIA 658 – Social Network Analysis - Final report Kanad Chatterjee
1. 1
BIA 658 – Social Network Analysis
Marketing Research Analysis using
Facebook Network
Instructor: Prof. Yasuaki Sakamoto
By: Kanad Chatterjee
Spring
2014
2. 2
Contents
Introduction
...........................................................................................................
3
Key
User
Identification
...........................................................................................
3
Connectors or Hubs (Influence parameter – Degree)..................................................4
Brokers or Bridges (Influence parameter – Betweenness) ..........................................5
Speed of Propagation (Influence Parameter – Closeness)..........................................6
Shortest Path between Nodes .....................................................................................7
Community
Identification
(Using
Facebook
likes
data)
...........................................
8
Attribute Addition to Nodes for Community Identification.............................................9
Community Identification – “Local Business” .............................................................10
Community Identification – “Small Business”.............................................................11
Geo-‐Specific
Analysis
............................................................................................
12
Country-wise Grouping ..............................................................................................13
Country-specific Network Extraction ..........................................................................14
Engagement
Quadrant
.........................................................................................
15
References
...........................................................................................................
16
3. 3
Introduction
In the present world as well as in the immediate foreseeable future the
influencing power that social networking websites such as Facebook and
Twitter have over their users can not be denied. These sites have become the
hotbeds for media campaigns ranging from consumer goods to elections.
Businesses have been prompt to cash in on the potential that these social
networking sites hold. More than 42% of B2B companies and almost 64%
B2C companies have acquired at least one major client through the use of
effective Facebook campaigns.
As part of this project we have therefore tried to come up with various
analyses that are specific to analyzing Facebook data, but could be
conveniently used for other such sites as well to identify core interest groups
for specific businesses and devise marketing and advertising strategies. The
analysis undertaken can be broadly grouped as:
• Key Users (Nodes) identification
§ Connectors or Hubs
§ Brokers
§ Speed of propagation
• Community Identification
• Geo-spatial analysis
• Engagement quadrant
The data used for the analysis is the personal Facebook data for the team
members (Kanad Chatterjee & Kanika Jain) and added Facebook data from
few of their friends and family obtained with their consent, through the use of
Netvizz application provided by Facebook. The data utilized for the analysis
are “Basic Data” (shows Users and connections amongst them) and the
“Likes” data (shows what various Users have liked and the for the items liked
their popularity). The intent is to be able to identify influential nodes who can
then be studied further to categorize them into potential consumers, partners,
suppliers etc.
Key
User
Identification
To effectively understand any social network and harness its power we need
to identify who clearly the roles that various users are playing in the network –
who are the leaders, influencers, connectors etc. We also need to be able to
answer questions such as - what clusters exist within the network and who
are in them? Who is (are) at the core of the network and who is at the
periphery?
4. 4
Connectors
or
Hubs
(Influence
parameter
–
Degree)
Degree of a node is the measure of the number of direct connections that the
node has with other nodes within the network. Therefore nodes with highest
degree are the most active and can be thought of as “Connectors or Hubs”.
These are the nodes that most effectively connect other nodes across the
network that are not directly connected to each other. In the figure below the
nodes are sized by their Degree measure giving us a clear picture of who the
top connectors are in this particular network. For instance, we observe that
“Gaurav Jain”, “Ashish Agrawal” and “Pallavi Vaid” are the top connectors in
terms of direct connections, meaning they would be most effective in
spreading information across the network.
Nodes sized by Degree to show top Connectors or Hubs
5. 5
Brokers
or
Bridges
(Influence
parameter
–
Betweenness)
Although the nodes with higher Degree measures have more direct
connections within the network, there are other nodes that might be better
placed in terms of location, measured by Betweenness Centrality. Nodes with
high betweenness have great influence over what does or does not flow over
the network. They can therefore be seen as information brokers and play a
crucial role in any social network. These are the people through which
majority of all information with pass through from one end of the network to
another. An interesting observation here is that though “Gaurav Jain” and
“Pallavi Vaid” both had more direct connections as compared to “Ashish
Agrawal, he has a higher betweenness suggesting that he would be better
placed to control the flow of information across the various communities.
Nodes sized by Betweenness Centrality to show top Brokers or Bridges
6. 6
Speed
of
Propagation
(Influence
Parameter
–
Closeness)
While Degree and Betweenness show which nodes have more influence in
terms of effectiveness and flow-control of information across the network,
another parameter, Closeness Centrality, defines how quickly a node will be
able to propagate the information across the network. The nodes with higher
Closeness Centrality will have the earliest visibility of any information flowing
through the network and will also be the quickest to spread any information
through the network, making them ideal candidates for blitz advertisement or
branding campaigns. For instance, in the figure below “Himanshu Upadhyay”,
“Namrata Lal” and “Vaibhav Jain” are the best propagators.
Nodes sized by Closeness Centrality to show top Propagators
7. 7
Shortest
Path
between
Nodes
As part of this project anything similar, the Facebook data from multiple users
network is combined to create a larger network. And therefore it could very
well happen that the businesses undertaking the analysis do not have any
existing connection whatsoever to the most influential nodes through any
other nodes. However, if such connections already exist it would prove
beneficial to identify the same and use them for possible referrals when going
in for any targeted advertisements or business pitches.
Shortest Path between any two nodes selected. Path shown is Directed from Kanika Jain to Ashish Agrawal
8. 8
Community
Identification
(Using
Facebook
likes
data)
Every user within the Facebook network generally builds up memberships to
some groups over the period of their subscription. These followership or
“likes” can be used to map out users whom we would like to target as part of
out marketing and advertising analysis.
The way we approached this area was to assign separate attribute
values to each User or Node based on the groups they expressed interest in.
This would ensure that Community identification is very clean and would also
help us study the various groups and their individual dynamics separately in
Gephi, using filters for the various groups that we might be interested in.
Another advantage of assigning multiple attributes to Users or Nodes using
groups is to be able to easily identify cross-pollinators across groups.
The attribute creation is accomplished by the way of writing simple
“Join” queries between the “Basic” and “Likes” user data, through the use of a
SQL database and queries.
9. 9
Attribute
Addition
to
Nodes
for
Community
Identification
Once the community like information has been converted to attributes for the
Nodes using the SQL queries, the same can be loaded into Gephi as shown
in the figure above. All of the columns “node_category1” to “node_category4”
represent the communities “Local Business”, “Small Business”, “Clothing” and
“Jewelry/watches” respectively.
10. 10
Community
Identification
–
“Local
Business”
The figure above shows the community “Local Business”, with the nodes
sized by “Degree” and coloured by countries. This has been achieved by
filtering the nodes based on the attribute “node_category1” that we created for
identifying this particular community using the method described just above.
This gives us insights into people who are interested in local businesses.
They might comprise of consumers, possible future partners or suppliers for
our own business. However, identification and segregation of users into such
groups will require further information and analysis, such as text analysis of
their like comments on Facebook, gathered through possible web scraping.
Nodes sized by Degree, Coloured by Country. Filtered on attribute node_category1=”Local Business”
11. 11
Community
Identification
–
“Small
Business”
Nodes sized by Closeness Centrality, Coloured by Country. Filtered on attribute node_category2=”SmallBusiness”
12. 12
Geo-‐Specific
Analysis
All social networks have an underlying spatial architecture and the
information flows through these geographically linked spaces often strongly
influences attitudes and behaviours. People interact with their neighbours
and the outcome of these interactions could be multifold e.g. change to their
perception of certain products or services (either positive or negative),
changes to their shopping patterns etc.
Therefore we would like to identify all the geographical locations that our
Facebook network consists of. The added advantage that Gephi provides us
is the ability to group the Users or Nodes based on their geographical
coordinates (Latitudes and Longitudes) using plugins such as “GeoLayout” or
“Map of the World”.
Now, we might not always have access to the exact location data for the
users, because the availability of the same depends on individual privacy
settings that users have on these social networks. However, in the absence
of such straightforward location information, it is often possible to derive the
same using some other attributes that are readily available. Here we have
pursued an approach wherein we have used the “locale” information provided
as part of the “likes” data from Facebook to derive the location information for
the users. The “locale” data is a combination of the ISO Language and
Country Codes respectively, concatenated using an underscore. The basic
format is “ll_CC”, where ll is a two-letter language code, and CC is a two-
letter country code. For instance, “en_US” represents U.S. English, “en_IN”
represents Indian English. For this project we have used a simple “IF”
function in excel to convert these “locales” into the respective country
information e.g. “en_US” translates to “USA”, “en_IN” translates to “India” etc.
Once we have the country attribute allocated to each node, we can bound all
such nodes within the latitudinal and longitudinal limits for each country. For
this project it was accomplished by using the RANDOM function in Excel with
inputs as the lowest and highest latitudes and longitudes for the country, e.g.
nodes with country as USA were bound between 24.52o
N latitude to 49.38o
N
latitude, and from approximately 66.95o
W longitude to 124.77o
W longitude.
13. 13
Country-‐wise
Grouping
Using the latitude and longitude information derived above we can group the
nodes by their respective countries using the “GeoLayout” plug-in for Gephi.
Once we have the above shown grouping of the Nodes by the countries, we
can use the Rectangular selection tool from Gephi to select the individual
nodes for a particular country and copy them to a new workspace within the
Gephi project. This exports both the Node information as well as all the
related Edges to the new workspace, effectively giving us a sub-graph for the
selected country (see figure below).
Thereafter all the analyses that have been described above can be run
against this country specific graph giving us geo-specific insights into
possible marketing and advertisement strategies.
Nodes grouped by Countries. Gephi plugin used is “GeoLayout”
14. 14
Country-‐specific
Network
Extraction
The figure above shows the network that we have for United Kingdom once
we pull all the Nodes for UK into a separate workspace. The nodes have
been sized by the “Degree” measure, giving us a clear picture of who the
most influential individuals are within this geography.
From this graph we also observe that the network within the UK geography is
fairly well connected. In effect that means this network has a small world
property and therefore information is going to propagate fairly quickly across
this network. Therefore advertising campaigns utilizing this network has a
chance of being fairly quick and effective.
Nodes grouped by Countries. Gephi plugin used is “GeoLayout”
15. 15
Engagement
Quadrant
The figure above gives us what we could term as an “Engagement
Quadrant”. We have “Closeness Centrality” (Speediness parameter) mapped
on the X-axis and “Degree” (Influence parameter) mapped on the Y-axis. And
the Nodes have been sized on “Betweenness Centrality”. Then the graph has
been divided into four quadrants to categorize the nodes into the four
categories as defined in the figure.
This quadrant helps us identify the relative importance of people within the
network based on multiple criteria and come up with engagement strategies
16. 16
accordingly. For instance, users in the “High Influence & High Propagator”
category could very well be targeted to run some incentivized marketing or
advertisement campaigns.
References
1. http://www.orgnet.com/sna.html
2. http://www.slideshare.net/gcheliotis/social-network-analysis-3273045
3. https://persuasionradio.wordpress.com/2010/05/06/using-netvizz-
gephi-to-analyze-a-facebook-network/
4. http://noduslabs.com/cases/russian-protest-network-analysis-
facebook-gephi-netvizz/
5. Hansen, Derek et al. (2010). Analyzing Social Media Networks with
NodeXL. Morgan Kaufmann. p. 32. ISBN 978-0-12-382229-1.