JPJ1419 Discovering Emerging Topics in Social Streams via Link-Anomaly Detec...chennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Social network has become so popular with overwhelming high rate of growth, due to this popularity the online social networks is facing the issues of spamming, which has leads to unsubstantial economic loss to this menace of spam and spammers activities. It has leads to uncontrollable dissemination of viruses and malwares, promotional ads, phishing, and scams. spam activities has enter a new dangerous dimension, the spammers have step up their games and tactics online social networks, it consumes large amounts of network bandwidth leading to less revenue and significant economic loss to both private and public sectors. From the previous scholars work on spammer classification taxonomy, various machine learning techniques have been extensively used to detect spam activities and spammers in online social networks. There are various classifier that are learn over content-based features extracted from the user's interactions and profiles to label them as spam/spammers or legitimate. But recently, new network structural bench mark features have been proposed for spammer detection task, but their importance using structural bench mark learning methods has not been extensively evaluated yet. In this research work, we evaluate the the metric performance of some structural bench mark learning methods using scientific and strategic approach based attributes extracted from an interaction network for the task of spammer detection in online social network.
This literature survey discusses papers on the topological structure of social networks and information propagation. Regarding network structure, papers found that social networks like Facebook exhibit scale-free and small-world properties with high clustering. Different networks may have different structures depending on factors like symmetry. Regarding information spread, factors like sender involvement, tie strength, and communication influence forwarding. Messages tend to spread through closely connected friendship networks rather than broadly. Key influencers and the structure of interaction graphs also impact propagation patterns.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
This is a presentation that describes at a high level some of the work we've been performing related to NodeXL and it's use to understand social media networks.
The document proposes a system called Filtered Wall (FW) to filter unwanted messages from users' walls in Online Social Networks (OSNs). FW uses machine learning techniques to automatically categorize short text messages. It also provides flexible filtering rules that allow users to customize which content is displayed on their walls based on message categorization, user profiles, and relationships. The system was experimentally evaluated on its ability to accurately categorize messages and effectively apply the filtering rules. A prototype was implemented for Facebook to demonstrate the system.
This document discusses analyzing social media networks using NodeXL. It defines social media and lists common types. It then covers key concepts in social network analysis including nodes, edges, metrics like centrality and density. NodeXL is introduced as a tool for visualizing and analyzing social networks from data collected from sources like personal emails, Twitter, forums and YouTube. Examples of social network analyses using NodeXL are provided such as mapping corporate email communication and identifying influencers on Twitter.
A system to filter unwanted messages from theMadan Golla
This document presents a system to filter unwanted messages from social network users' walls. It consists of three main components: filtering rules, thresholds for applying the rules which are customized for each user, and a blacklist mechanism. The filtering rules allow users to control what types of messages are allowed on their walls based on attributes of the message creator and their relationship to the user. The system aims to provide flexible and transparent filtering of messages while minimizing mistakes.
JPJ1419 Discovering Emerging Topics in Social Streams via Link-Anomaly Detec...chennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Social network has become so popular with overwhelming high rate of growth, due to this popularity the online social networks is facing the issues of spamming, which has leads to unsubstantial economic loss to this menace of spam and spammers activities. It has leads to uncontrollable dissemination of viruses and malwares, promotional ads, phishing, and scams. spam activities has enter a new dangerous dimension, the spammers have step up their games and tactics online social networks, it consumes large amounts of network bandwidth leading to less revenue and significant economic loss to both private and public sectors. From the previous scholars work on spammer classification taxonomy, various machine learning techniques have been extensively used to detect spam activities and spammers in online social networks. There are various classifier that are learn over content-based features extracted from the user's interactions and profiles to label them as spam/spammers or legitimate. But recently, new network structural bench mark features have been proposed for spammer detection task, but their importance using structural bench mark learning methods has not been extensively evaluated yet. In this research work, we evaluate the the metric performance of some structural bench mark learning methods using scientific and strategic approach based attributes extracted from an interaction network for the task of spammer detection in online social network.
This literature survey discusses papers on the topological structure of social networks and information propagation. Regarding network structure, papers found that social networks like Facebook exhibit scale-free and small-world properties with high clustering. Different networks may have different structures depending on factors like symmetry. Regarding information spread, factors like sender involvement, tie strength, and communication influence forwarding. Messages tend to spread through closely connected friendship networks rather than broadly. Key influencers and the structure of interaction graphs also impact propagation patterns.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
This is a presentation that describes at a high level some of the work we've been performing related to NodeXL and it's use to understand social media networks.
The document proposes a system called Filtered Wall (FW) to filter unwanted messages from users' walls in Online Social Networks (OSNs). FW uses machine learning techniques to automatically categorize short text messages. It also provides flexible filtering rules that allow users to customize which content is displayed on their walls based on message categorization, user profiles, and relationships. The system was experimentally evaluated on its ability to accurately categorize messages and effectively apply the filtering rules. A prototype was implemented for Facebook to demonstrate the system.
This document discusses analyzing social media networks using NodeXL. It defines social media and lists common types. It then covers key concepts in social network analysis including nodes, edges, metrics like centrality and density. NodeXL is introduced as a tool for visualizing and analyzing social networks from data collected from sources like personal emails, Twitter, forums and YouTube. Examples of social network analyses using NodeXL are provided such as mapping corporate email communication and identifying influencers on Twitter.
A system to filter unwanted messages from theMadan Golla
This document presents a system to filter unwanted messages from social network users' walls. It consists of three main components: filtering rules, thresholds for applying the rules which are customized for each user, and a blacklist mechanism. The filtering rules allow users to control what types of messages are allowed on their walls based on attributes of the message creator and their relationship to the user. The system aims to provide flexible and transparent filtering of messages while minimizing mistakes.
Categorize balanced dataset for troll detectionvivatechijri
As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.
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
This document summarizes a research project on predicting whether online content will become viral based on its spread through the core-periphery structure of social networks. The research aims to improve on prior work focusing on community structure by also considering how content spreads between the dense core and less connected periphery of networks. It involves generating a synthetic social network model with scale-free, community, and core-periphery properties, developing a model for content spread, and comparing the approach to real networks. The hypothesis is that for content to spread widely across communities and become viral, it must first reach the highly connected core nodes.
Presentation on the draft manuscript 'A systematic literature review of academic cyberbullying- notable research absences in Higher Education contexts' given to the Design Research Activities Workgroup at CPUT
This document discusses how rumors spread quickly through social networks. It simulates a simple rumor spreading process on real-world social networks like Twitter and Orkut as well as theoretical network models. The results show that rumors spread much faster in the structures of actual social networks and preferential attachment networks than in random or complete networks. Specifically, a rumor reaching 45.6 million Twitter users within 8 rounds of communication.
CISummit 2013: Luke Matthews, Tracking the Electronic Metadata Trail of the S...Steven Wardell
The document discusses how electronic metadata from social networks can be tracked to analyze people's social lives and relationships. Metadata contains information about people's online behavior and interactions that provide insights into how they form, maintain and dissolve social ties. Network analysis of metadata trails can reveal people's roles in social networks as brokers of information or top connectors. Comparing metadata-based networks to survey-based networks shows they often produce similar results and insights into factors that create "silos" between groups. Algorithms can also map large networks of people using aggregated metadata information.
This document summarizes a study that quantifies information overload on social media platforms using data from Twitter. The study models social media users as information processing systems that receive information in queues and process it at certain rates. By analyzing timestamps of tweets received and forwarded, the study estimates users' information processing behaviors and limits. Key findings include evidence that most users have processing limits of around 30 tweets/hour, and that overloaded users take longer to process information and prioritize tweets from select sources. The study also finds that information overload reduces the effectiveness of information spreading on social issues.
The paper analyzes the relationship between people's social networks and personal behaviors using data from over 10 million people. It finds that people who chat with each other are more likely to share interests and characteristics like age, gender, and location. Those who spend more time chatting show stronger correlations in interests. Similar findings hold for people connected through shared friends. The paper uses mathematical models to establish these correlations between social connections and personal attributes and behaviors.
The document presents a two-layer epidemic model for analyzing malware propagation in large-scale networks. The model calculates how many networks have been compromised over time based on the susceptible-infected model, and then calculates how many hosts within each compromised network have been infected. Theoretical analysis of the model finds that malware distribution follows an exponential distribution early on, a power law distribution with a short exponential tail later, and a pure power law distribution finally. Experiments on real-world Android and Conficker malware datasets confirm these theoretical findings. The two-layer model provides a better representation of malware propagation in large-scale networks compared to traditional single-layer epidemic models.
Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date.
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
seminar on To block unwanted messages _from osnShailesh kumar
The document summarizes a seminar on blocking unwanted messages from online social networks. It discusses the need for filtering spam, phishing, and malware attacks on social media. It proposes a filtered wall architecture, which is a three-tier structure consisting of a social network manager, social network application, and graphical user interface. The social network application includes content-based and short text classification to categorize messages. Filtering rules and blacklists are used to filter unwanted messages on the graphical user interface's filtered wall. The system aims to improve filtering of undesirable content from users' social media walls.
Filter unwanted messages from walls and blocking non legitimate users in osnIAEME Publication
1. The document presents a system to filter unwanted messages from user walls in online social networks. It aims to give users more control over the content that appears on their walls.
2. A machine learning classifier is used to automatically label messages by category. Users can then specify filtering rules to block certain categories or keywords from appearing.
3. The system also implements a blacklist to temporarily or permanently block users who frequently post unwanted content, as determined by filtering rules and a threshold.
2010 june - personal democracy forum - marc smith - mapping political socia...Marc Smith
This document introduces Marc Smith and his work analyzing social networks. It provides biographical information on Smith and describes some of the tools he has created for social network analysis, including NodeXL. NodeXL is a free social network analysis plugin for Excel that allows users to import and analyze data from social media sources. The document also provides examples of NodeXL network maps and analyses that Smith has conducted on social media discussions around topics like the 2010 Gulf oil spill.
Distributed Link Prediction in Large Scale Graphs using Apache SparkAnastasios Theodosiou
Online social networks (OSNs) such as Facebook, Instagram, Twitter, and LinkedIn are aware of high acceptance since they enable users to share digital content (links, pictures, videos), express or share opinions and expand their social circle, by making new friends. All these kinds of interactions that users participate in the lead to the evolution and expansion of social networks over time. OSNs support users, providing them with friend recommendations based on the existing explicit friendship network but also to their preferences resulting from their interaction with the net which they gradually build. Link prediction methods try to predict the likelihood of a future connection between two nodes in a given network. This can be used in biological networks to infer protein-protein interactions or to suggest possible friends to a user in an OSN. In e-commerce it can help to build recommendation systems such as the "people who bought this may also buy" feature, e.g., on Amazon and in the security domain link prediction can help to identify hidden groups of people who use potential violence and criminals. Due to the massive amounts of data that is collected today, the need for scalable approaches arises to this problem. The purpose of this diploma thesis is to experiment and use various techniques of machine learning, both supervised and unsupervised, to predict links to a network of academic papers using document similarity metrics based on the characteristics of the nodes but also other structural features, based on the network. Experimentation and implementation of the application took place using Apache Spark to manage the large data volume using the Scala programming language.
Enhancing C-Span Video Archive with Practice Capital Metadata and data journa...Sorin Adam Matei
The presentation argues that the C-Span archive is not a mere repository of moving pictures. It can also be seen as a one of a kind “big data” repository. If processed from a “practice capital” perspective with quantitative and network analytic tools, such data can significantly extend the capabilities of C-Span archives by identifying the central actors in a debate and their ability to sway it. The proposed approach may serve the public interest though API tools that support third party development of visualization and analytic apps, which can lead to more informed debates and new forms of data driven journalism.
Jacque lewis - Senior Project -w/o scriptJacque Lewis
This 12-minute mini-documentary explores whether social networks should enforce stricter regulations regarding social media harassment. The documentary features interviews with four professionals who discuss cyberbullying: Deborah Gonzalez, Jennifer Perry, Dr. Valerie Mason-John, and Shelia Mae. Through these interviews, the documentary seeks to answer its research question of whether social networks are responsible for enforcing their own rules against harassment and if those rules need to be stricter. Statistics about cyberbullying are also included throughout the documentary to provide context to the discussion. The documentary was created using Adobe Premiere to edit interview clips together with supplemental b-roll footage and was published on YouTube.
Slides for my presentation at the Digital Sociology mini-conference at the annual meeting of the Eastern Sociological Society, March 17, 2016 in Boston, MA
Discovering emerging topics in social streams via link anomaly detectionFinalyear Projects
This document proposes a method to detect emerging topics in social media streams by analyzing anomalies in how users mention and link to each other, rather than analyzing textual content. It presents a probability model to capture normal user mentioning behavior and detect anomalies. Anomaly scores from many users are aggregated and analyzed with change point detection to identify when new topics emerge. The method is tested on real Twitter data and shown to detect emerging topics as early or earlier than text-based methods, especially when textual keywords are ambiguous.
Categorize balanced dataset for troll detectionvivatechijri
As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.
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
This document summarizes a research project on predicting whether online content will become viral based on its spread through the core-periphery structure of social networks. The research aims to improve on prior work focusing on community structure by also considering how content spreads between the dense core and less connected periphery of networks. It involves generating a synthetic social network model with scale-free, community, and core-periphery properties, developing a model for content spread, and comparing the approach to real networks. The hypothesis is that for content to spread widely across communities and become viral, it must first reach the highly connected core nodes.
Presentation on the draft manuscript 'A systematic literature review of academic cyberbullying- notable research absences in Higher Education contexts' given to the Design Research Activities Workgroup at CPUT
This document discusses how rumors spread quickly through social networks. It simulates a simple rumor spreading process on real-world social networks like Twitter and Orkut as well as theoretical network models. The results show that rumors spread much faster in the structures of actual social networks and preferential attachment networks than in random or complete networks. Specifically, a rumor reaching 45.6 million Twitter users within 8 rounds of communication.
CISummit 2013: Luke Matthews, Tracking the Electronic Metadata Trail of the S...Steven Wardell
The document discusses how electronic metadata from social networks can be tracked to analyze people's social lives and relationships. Metadata contains information about people's online behavior and interactions that provide insights into how they form, maintain and dissolve social ties. Network analysis of metadata trails can reveal people's roles in social networks as brokers of information or top connectors. Comparing metadata-based networks to survey-based networks shows they often produce similar results and insights into factors that create "silos" between groups. Algorithms can also map large networks of people using aggregated metadata information.
This document summarizes a study that quantifies information overload on social media platforms using data from Twitter. The study models social media users as information processing systems that receive information in queues and process it at certain rates. By analyzing timestamps of tweets received and forwarded, the study estimates users' information processing behaviors and limits. Key findings include evidence that most users have processing limits of around 30 tweets/hour, and that overloaded users take longer to process information and prioritize tweets from select sources. The study also finds that information overload reduces the effectiveness of information spreading on social issues.
The paper analyzes the relationship between people's social networks and personal behaviors using data from over 10 million people. It finds that people who chat with each other are more likely to share interests and characteristics like age, gender, and location. Those who spend more time chatting show stronger correlations in interests. Similar findings hold for people connected through shared friends. The paper uses mathematical models to establish these correlations between social connections and personal attributes and behaviors.
The document presents a two-layer epidemic model for analyzing malware propagation in large-scale networks. The model calculates how many networks have been compromised over time based on the susceptible-infected model, and then calculates how many hosts within each compromised network have been infected. Theoretical analysis of the model finds that malware distribution follows an exponential distribution early on, a power law distribution with a short exponential tail later, and a pure power law distribution finally. Experiments on real-world Android and Conficker malware datasets confirm these theoretical findings. The two-layer model provides a better representation of malware propagation in large-scale networks compared to traditional single-layer epidemic models.
Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date.
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
seminar on To block unwanted messages _from osnShailesh kumar
The document summarizes a seminar on blocking unwanted messages from online social networks. It discusses the need for filtering spam, phishing, and malware attacks on social media. It proposes a filtered wall architecture, which is a three-tier structure consisting of a social network manager, social network application, and graphical user interface. The social network application includes content-based and short text classification to categorize messages. Filtering rules and blacklists are used to filter unwanted messages on the graphical user interface's filtered wall. The system aims to improve filtering of undesirable content from users' social media walls.
Filter unwanted messages from walls and blocking non legitimate users in osnIAEME Publication
1. The document presents a system to filter unwanted messages from user walls in online social networks. It aims to give users more control over the content that appears on their walls.
2. A machine learning classifier is used to automatically label messages by category. Users can then specify filtering rules to block certain categories or keywords from appearing.
3. The system also implements a blacklist to temporarily or permanently block users who frequently post unwanted content, as determined by filtering rules and a threshold.
2010 june - personal democracy forum - marc smith - mapping political socia...Marc Smith
This document introduces Marc Smith and his work analyzing social networks. It provides biographical information on Smith and describes some of the tools he has created for social network analysis, including NodeXL. NodeXL is a free social network analysis plugin for Excel that allows users to import and analyze data from social media sources. The document also provides examples of NodeXL network maps and analyses that Smith has conducted on social media discussions around topics like the 2010 Gulf oil spill.
Distributed Link Prediction in Large Scale Graphs using Apache SparkAnastasios Theodosiou
Online social networks (OSNs) such as Facebook, Instagram, Twitter, and LinkedIn are aware of high acceptance since they enable users to share digital content (links, pictures, videos), express or share opinions and expand their social circle, by making new friends. All these kinds of interactions that users participate in the lead to the evolution and expansion of social networks over time. OSNs support users, providing them with friend recommendations based on the existing explicit friendship network but also to their preferences resulting from their interaction with the net which they gradually build. Link prediction methods try to predict the likelihood of a future connection between two nodes in a given network. This can be used in biological networks to infer protein-protein interactions or to suggest possible friends to a user in an OSN. In e-commerce it can help to build recommendation systems such as the "people who bought this may also buy" feature, e.g., on Amazon and in the security domain link prediction can help to identify hidden groups of people who use potential violence and criminals. Due to the massive amounts of data that is collected today, the need for scalable approaches arises to this problem. The purpose of this diploma thesis is to experiment and use various techniques of machine learning, both supervised and unsupervised, to predict links to a network of academic papers using document similarity metrics based on the characteristics of the nodes but also other structural features, based on the network. Experimentation and implementation of the application took place using Apache Spark to manage the large data volume using the Scala programming language.
Enhancing C-Span Video Archive with Practice Capital Metadata and data journa...Sorin Adam Matei
The presentation argues that the C-Span archive is not a mere repository of moving pictures. It can also be seen as a one of a kind “big data” repository. If processed from a “practice capital” perspective with quantitative and network analytic tools, such data can significantly extend the capabilities of C-Span archives by identifying the central actors in a debate and their ability to sway it. The proposed approach may serve the public interest though API tools that support third party development of visualization and analytic apps, which can lead to more informed debates and new forms of data driven journalism.
Jacque lewis - Senior Project -w/o scriptJacque Lewis
This 12-minute mini-documentary explores whether social networks should enforce stricter regulations regarding social media harassment. The documentary features interviews with four professionals who discuss cyberbullying: Deborah Gonzalez, Jennifer Perry, Dr. Valerie Mason-John, and Shelia Mae. Through these interviews, the documentary seeks to answer its research question of whether social networks are responsible for enforcing their own rules against harassment and if those rules need to be stricter. Statistics about cyberbullying are also included throughout the documentary to provide context to the discussion. The documentary was created using Adobe Premiere to edit interview clips together with supplemental b-roll footage and was published on YouTube.
Slides for my presentation at the Digital Sociology mini-conference at the annual meeting of the Eastern Sociological Society, March 17, 2016 in Boston, MA
Discovering emerging topics in social streams via link anomaly detectionFinalyear Projects
This document proposes a method to detect emerging topics in social media streams by analyzing anomalies in how users mention and link to each other, rather than analyzing textual content. It presents a probability model to capture normal user mentioning behavior and detect anomalies. Anomaly scores from many users are aggregated and analyzed with change point detection to identify when new topics emerge. The method is tested on real Twitter data and shown to detect emerging topics as early or earlier than text-based methods, especially when textual keywords are ambiguous.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses machine learning techniques for filtering unwanted messages in online social networks. It proposes a content-based filtering system that allows users to control the messages posted on their walls by filtering out unwanted messages. The system uses a machine learning-based classifier to automatically categorize short text messages based on their content. It also includes a blacklist feature to block specific users from posting if they consistently share unwanted messages. The goal is to give users better control over their social media experience by reducing noise and unwanted content on their walls.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
This dissertation analyzes social media data and outlines approaches for understanding online communication and collaboration. It presents algorithms for detecting communities using structural and semantic properties. It analyzes blog subscription patterns and the microblogging phenomenon. Systems are developed for opinion retrieval from blogs and identifying influential users. The growth of social media and tagging behavior are also studied through analysis of tags and social graphs.
Filtering Unwanted Messages from Online Social Networks (OSN) using Rule Base...IOSR Journals
Online Social Networks (OSNs) are today one of the most popular interactive medium to share,
communicate, and distribute a significant amount of human life information. In OSNs, information filtering can
also be used for a different, more responsive, function. This is owing to the fact that in OSNs there is the
possibility of posting or commenting other posts on particular public/private regions, called in general walls.
Information filtering can therefore be used to give users the ability to automatically control the messages
written on their own walls, by filtering out unwanted messages. OSNs provide very little support to prevent
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Volume URL: https://airccse.org/journal/ijc2022.html
Abstract URL:https://aircconline.com/abstract/ijcnc/v14n5/14522cnc05.html
Pdf URL: https://aircconline.com/ijcnc/V14N5/14522cnc05.pdf
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discovering emerging topics in social
1. Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
Discovering Emerging Topics in Social Streams via Link-
Anomaly Detection
Detection of emerging topics is now receiving renewed interest motivated by the rapid growth of
social networks. Conventional-term- frequency-based approaches may not be appropriate in this
context, because the information exchanged in social-network posts include not only text but also
images, URLs, and videos. We focus on emergence of topics signaled by social aspects of theses
networks. Specifically, we focus on mentions of user links between users that are generated
dynamically (intentionally or unintentionally) through replies, mentions, and retweets. We
propose a probability model of the mentioning behavior of a social network user, and propose to
detect the emergence of a new topic from the anomalies measured through the model.
Aggregating anomaly scores from hundreds of users, we show that we can detect emerging
topics only based on the reply/mention relationships in social-network posts. We demonstrate
our technique in several real data sets we gathered from Twitter. The experiments show that the
proposed mention-anomaly-based approaches can detect new topics at least as early as text-anomaly-
based approaches, and in some cases much earlier when the topic is poorly identified
by the textual contents in posts.
A new (emerging) topic is something people feel like discussing, commenting, or
forwarding the information further to their friends. Conventional approaches for topic
detection have mainly been concerned with the frequencies of (textual) words.
DISADVANTAGES OF EXISTING SYSTEM:
A term- frequency-based approach could suffer from the ambiguity caused by synonyms or
homonyms. It may also require complicated preprocessing (e.g., segmentation) depending on the
target language. Moreover, it cannot be applied when the contents of the messages are mostly
nontextual information. O n the other hand, the “words” formed by mentions are unique, require
Contact: 9703109334, 9533694296
ABSTRACT:
EXISTING SYSTEM:
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
2. Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
little preprocessing to obtain (the information is often separated from the contents), and are
available regardless of the nature of the contents.
In this paper, we have proposed a new approach to detect the emergence of topics in a
The basic idea of our approach is to focus on the social aspect of the posts reflected in the
mentioning behavior of users instead of the textual contents.
We have proposed a probability model that captures both the number of mentions per
post and the frequency of mentionee.
ADVANTAGES OF PROPOSED SYSTEM:
The proposed method does not rely on the textual contents of social network posts, it is
robust to rephrasing and it can be applied to the case where topics are concerned with
information other than texts, such as images, video, audio, and so on.
The proposed link-anomaly-based methods performed even better than the keyword-based
methods on “NASA” and “BBC” data sets.
Contact: 9703109334, 9533694296
PROPOSED SYSTEM:
social network stream.
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
3. Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
Contact: 9703109334, 9533694296
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
4. Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
SOFTWARE REQUIREMENTS:
Operating system : Windows XP/7.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4
Database : MYSQL
Toshimitsu Takahashi, Ryota Tomioka, and Kenji Yamanishi, Member, IEEE,“Discovering
Emerging Topics in Social Streams via Link-Anomaly Detection”, IEEE TRANSACTIONS
ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 1, JANUARY 2014.
Contact: 9703109334, 9533694296
REFERENCE:
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in