Combining topology-based & content-based analysis for followee recommendation on Twitter

Download
index.pdf
2015
Yanar, Aysu
Show full item record
289
views
128
downloads
Twitter has become an important social platform for individuals and people share a high number of information about their personal lives, interests and viral news during emergencies. As of 2014, Twitter has 240 million active users and approximately 500 million tweets are shared every day. This information overload in Twitter has become a serious problem due to the growing volume of messages and increasing number of users. Recommender systems help to overcome this challenge. Finding interesting users and getting useful information from micro-blogging sites has become difficult since the mass of the data contains irrelevant messages, promotions and spam. In this thesis we propose a followee recommender system to overcome this problem. Recommendation in Twitter has been studied by several researchers and promising results have been achieved. In this thesis, we combine topological approaches and content- based analysis within the scope of English and Turkish language to find relevant followees for Twitter users. We propose seven different strategies by using different aspects of Twitter. Personalized recommendations have been generated for 22 active Twitter users. In order to increase effectiveness of recommendations, real Twitter data has been used. The experimental results show that using retweet data gives better recommendations than favorite data and we have achieved 0.79 success rate when we combine the topological features of Twitter.
Recommender systems (Information filtering)., Information filtering systems., Information retrieval., Sentimentalism., Data mining., Topology.
http://etd.lib.metu.edu.tr/upload/12618713/index.pdf
https://hdl.handle.net/11511/24616
Graduate School of Informatics, Thesis

Suggestions

FACT EXTRACTION AND VERIFICATION PIPELINE FOR COVID-19 RELATED USER POSTS IN SOCIAL MEDIA
Temiz, Orkun; Taşkaya Temizel, Tuğba; Department of Bioinformatics (2022-6-29)
Social media has become a prevalent platform for consuming and sharing information online. The vast amounts of information, shared easily and rapidly by social media, have increased the demand for fact-checking. Misinformation threatens not only the reputation of individuals and organizations but also society. When the COVID-19 pandemic broke out, the concerns around misinformation, which threatens public health and society, have significantly increased. In this thesis, a new zero-shot fact extraction and v...
Developing recommendation techniques for location based social networks using random walk
Bağcı, Hakan; Karagöz, Pınar; Department of Computer Engineering (2015)
The location-based social networks (LBSN) enable users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this thesis, we propose three recommendation algorithms for location-based social networks. These are random walk based context-aware location (CLoRW), activity (RWCAR) and friend (RWCFR) recommendation algorithms. All the algorithms consider the current context (i.e. curre...
Analyzing and Predicting Privacy Settings in the Social Web
Naini, Kaweh Djafari; Altıngövde, İsmail Sengör; Kawase, Ricardo; Herder, Eelco; Niederee, Claudia (2015-07-03)
Social networks provide a platform for people to connect and share information and moments of their lives. With the increasing engagement of users in such platforms, the volume of personal information that is exposed online grows accordingly. Due to carelessness, unawareness or difficulties in defining adequate privacy settings, private or sensitive information may be exposed to a wider audience than intended or advisable, potentially with serious problems in the private and professional life of a user. Alt...
Efficient rating estimation by using similarity in multi-dimensional check-in data
Uçar, Behlül; Karagöz, Pınar; Toroslu, İ. Hakkı; Department of Computer Engineering (2014)
The usage coverage of location based social networks have boomed in the last years as well as the amount of data produced in them. This data is suitable for processing in order to make prediction. One of the requirements of this process is that the method used should be suitable for very big data sets. We propose a graph-based similarity calculation method in location-based social networks which improves the rating prediction performance of Singular Value Decomposition based collaborative filtering systems....
Determining user types from twitter account contentand structure
Gürlek, Mesut; Toroslu, İsmail Hakkı; Department of Computer Engineering (2021-3-05)
People are using social media platforms more and more every day; hence, they are be-coming suitable for research studies by their rich content. Twitter is one of the biggestand most widely used social media platforms, and many studies focus on Twitter forsocial media research. In this thesis, we propose methodologies for determining usertypes of Twitter accounts by their metadata, content, and structure. Our first problemis classifying organization vs. individual account types using only metadata. After weg...
Citation Formats
A. Yanar, “Combining topology-based & content-based analysis for followee recommendation on Twitter,” M.S. - Master of Science, Middle East Technical University, 2015.
METU IIS - Integrated Information Service open@metu.edu.tr