On profiling bots in social media

Submit request for dataset at https://larc.smu.edu.sg/twitter-bot-profiling</p

Twitter bot detection using deep learning

Social media platforms have revolutionized how people interact with each other and how people gain information. However, social media platforms such as Twitter and Facebook quickly became the platform for public manipulation and spreading or amplifying political or ideological misinformation. Although malicious content can be shared by individuals, today millions of individual and coordinated automated accounts exist, also called bots which share hate, spread misinformation and manipulate public opinion without any human intervention. The work presented in this paper aims at designing and implementing deep learning approaches that successfully identify social media bots. Moreover we show that deep learning models can yield an accuracy of 0.9 on the PAN 2019 Bots and Gender Profiling dataset. In addition, the findings of this work also show that pre-trained models will be able to improve the accuracy of deep learning models and compete with Classical Machine Learning methods even on limited dataset

On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls

[EN] Many social media frauds related to finance can be summarized under what we consider promote-hit-and-run scams. Examples include rug pull scams also known as exit scams, pump-and-dump schemes or bogus crypto currency trading platforms. For scams of this kind to work they must be publicly advertised as lucrative investment opportunities disguising the fraudulent motivation behind them. Social media are key in this promotion. Here, fraudsters find platforms to persuade others investing into what later turns out to be a scam. Via social network analysis of Twitter screen names and their first-level contacts, our work investigates rug pulls. It is aimed at profiling social media communication around them with a special focus on the deployment of bots. Repeatedly bots have been identified in social media campaigns (Orabi et al., 2020). Bot deployment in the context of rug pulls, however, has not been studied yet. Our analysis of social data of 27 rug pulls reveals massive bot activity coordinated within and between rug pulls mainly targeting established finance news outlets, e.g., Bloomberg, Reuters. Among the conclusions of our work is that bot deployment may prove an early indicator for rug pulls and other promote-hit-and-run scams.Federal Ministry of Education and Research of Germany (BMBF)Janetzko, D.; Krauß, J.; Haase, F.; Rath, O. (2023). On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls. Editorial Universitat Politècnica de València. 187-194. https://doi.org/10.4995/CARMA2023.2023.1642818719

A Combined Synchronization Index for Grassroots Activism on Social Media

Social media has provided a citizen voice, giving rise to grassroots collective action, where users deploy a concerted effort to disseminate online narratives and even carry out offline protests. Sometimes these collective action are aided by inorganic synchronization, which arise from bot actors. It is thus important to identify the synchronicity of emerging discourse on social media and the indications of organic/inorganic activity within the conversations. This provides a way of profiling an event for possibility of offline protests and violence. In this study, we build on past definitions of synchronous activity on social media -- simultaneous user action -- and develop a Combined Synchronization Index (CSI) which adopts a hierarchical approach in measuring user synchronicity. We apply this index on six political and social activism events on Twitter and analyzed three action types: synchronicity by hashtag, URL and @mentions.The CSI provides an overall quantification of synchronization across all action types within an event, which allows ranking of a spectrum of synchronicity across the six events. Human users have higher synchronous scores than bot users in most events; and bots and humans exhibits the most synchronized activities across all events as compared to other pairs (i.e., bot-bot and human-human). We further rely on the harmony and dissonance of CSI-Network scores with network centrality metrics to observe the presence of organic/inorganic synchronization. We hope this work aids in investigating synchronized action within social media in a collective manner

Tracking Gendered Streams

One of the most prominent features of digital music services is the provision of personalized music recommendations that come about through the profiling of users and audiences. Based on a range of “bot experiments,” this article investigates if, and how, gendered patterns in music recommendations are provided by the streaming service Spotify. While our experiments did not give any strong indications that Spotify assigns different taste profiles to male and female users, the study showed that male artists were highly overrepresented in Spotify’s music recommendations; an issue which we argue prompts users to cite hegemonic masculine norms within the music industries. Although the results should be approached as historically and contextually contingent, we argue that they point to how gender and gendered tastes may be constituted through the interplay between users and algorithmic knowledge-making processes, and how digital content delivery may maintain and challenge gender relations and gendered power differentials within the music industries. Seen through the lens of critical research on software, music and gender performativity, the experiments thus provide insights into how gender is shaped and attributed meaning as it materializes in contemporary music streams

Tracking Gendered Streams

One of the most prominent features of digital music services is the provision of personalized music recommendations that come about through the profiling of users and audiences. Based on a range of “bot experiments,” this article investigates if, and how, gendered patterns in music recommendations are provided by the streaming service Spotify. While our experiments did not give any strong indications that Spotify assigns different taste profiles to male and female users, the study showed that male artists were highly overrepresented in Spotify’s music recommendations; an issue which we argue prompts users to cite hegemonic masculine norms within the music industries. Although the results should be approached as historically and contextually contingent, we argue that they point to how gender and gendered tastes may be constituted through the interplay between users and algorithmic knowledge-making processes, and how digital content delivery may maintain and challenge gender relations and gendered power differentials within the music industries. Seen through the lens of critical research on software, music and gender performativity, the experiments thus provide insights into how gender is shaped and attributed meaning as it materializes in contemporary music streams