Multirelational Twitter Bot Detection using Graph Neural Networks
2024
- 54Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage54
- Abstract Views54
Thesis / Dissertation Description
Social media is a key resource in modern human communication as well as for information. Ease of access and global reach is a primary factor to the popularity of several social media platforms like Twitter. Social Media Bots are automated programs which are developed for social engagement. These bots, however, are being used with malicious intent as well, to spread fake news and manipulate the masses. Identification of social media bot accounts has become crucial since social media has become one of the primary sources of news and information for a lot of people. This project aims to propose Multirelation Bot Detection Graph Neural Network methods (MultiBotGNNs)to solve the bot detection as a node classification problem. We incorporate ideas from existing techniques for bot identification and use Natural Language Processing, and Deep Learning. This project is developing an approach to identify social media bots by using the user characteristics, their social media activities, the user account bio processed with NLP along with the social network graphs of different user relations, as follow, follower, and like. We experimented with the TwiBot22, which is a big dataset of bots. Our focus was to consider the structures of the relations for the prediction, and ignoring the tweet contents and show that we can good results without the extra process time for the tweets.
Bibliographic Details
San Jose State University Library
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