Social media platforms have become immensely popular over the years, leading to significant changes in cyberspace and the emergence of numerous challenges. These challenges have various faces, such as disinformation, online hate, cyberbullying, discrimination, biases, and other facets of harm. From the perspective of an end-user, the modern-age online ecosystem can be harmful in various ways, e.g., by consistently coming across disinformation in the online spaces or being targeted by a hate attack because of a specific ethnic or racial background. As we move forward, it is crucial to understand the nature and impact of new-age harms to make the Internet a safer place for everyone.
To this end, my first contribution is the study of inauthentic accounts, also known as troll accounts. Troll accounts on social media are often sponsored by state actors aiming to manipulate public opinion on sensitive political topics. The strategy they commonly use is to interact with one another and appear innocuous to a regular user while covertly being used to spread toxic content and/or disinformation. I first study the effect that troll accounts have on online discussions on Reddit and show that state-sponsored troll accounts on Reddit produce threads that attract more toxic comments than other posts on the same subreddit.
Next, I build TROLLMAGNIFIER, a detection system for troll accounts based on the observation that these accounts often exhibit loose coordination and interact with each other to advance specific narratives. TROLLMAGNIFIER learns the typical behavior of known troll accounts and identifies more that behave similarly. I show that using TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided by Reddit by over 300%.
Building upon the understanding of troll accounts and online campaigns, I then study the broader aspects of online disinformation. In this work, I study 19 influence campaigns on Twitter originating from various countries and identify several strategies adopted across different state actors, e.g., using scheduling services to delegate their posting tasks, utilizing fake third-party versions of popular applications (e.g., “Twitter for Android”) to post messages, extensively retweeting to push certain agendas, and posting innocuous messages (e.g., motivational quotes) to potentially avoid detection. Overall, I identify several universal traits among campaigns to create a cross-campaign detection system that can detect upto 94% accounts from unseen campaigns.
Lastly, I delve deeper into the importance of cybersafety and study coordinated attacks, such as cyber-aggression and hate attacks, which are becoming increasingly common on video sharing networks like YouTube. Polarized online communities choose targets on prominent online platforms (e.g., YouTube) and organize their attacks by sending hateful messages to their target. The proposed system, TUBERAIDER, addresses this issue by automating the detection and attribution of attacks to their source communities, aiding in moderation, and understanding the motivations behind such actions. The system collects YouTube video links from diverse sources, including 4chan’s /pol/ board, r/The_Donald subreddit, and 16 incel subreddits. The attribution is performed through a machine learning classifier based on TF-IDF scores of important keywords and achieves an accuracy above 75% in attributing a coordinated attack to a given video.
In summary, my research focuses on understanding, detecting, and combating online harms using a data-driven approach. I develop tools to mitigate the malicious behavior with the goal of offering policymakers guidelines to ensure user safety on social media platforms.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/48873 |
| Date | 24 May 2024 |
| Creators | Saeed, Mohammad Hammas |
| Contributors | Stringhini, Gianluca |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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