Platform Independent Experiments On Social Media Semantic Scholar

Changing algorithms with artificial intelligence tools can influence partisan animosity. A web-based method was shown to mitigate political polarization on X by nudging antidemocratic and extremely negative partisan posts lower in a user’s feed. The tool, which is independent of the platform, has the potential to give users more say over what they see on social media.iStock A new tool shows it is possible to turn down the partisan rancor in an X feed — without removing political posts and without the direct cooperation of the platform. The study, from researchers at the University of Washington, Stanford University and Northeastern University, also indicates that it may one day be possible to let users take control of their social media algorithms. The researchers created a seamless, web-based tool that reorders content to move posts lower in a user’s feed when they contain antidemocratic attitudes and partisan animosity, such as advocating for violence or jailing supporters...

Researchers published their findings Nov. 27 in Science. We provide a practical guide to designing, conducting, and analyzing experiments using social media platforms. First, we discuss the benefits and challenges of using the targeting capabilities of advertisements on social media to recruit participants for a large class of experiments. Next, we outline the different types of interventions and their advantages and disadvantages. Finally, we summarize available compliance and outcome data, as well as the main limitations and challenges involved in the design and analysis of social media experiments.

Throughout, we provide technical details that are helpful when implementing these experiments. Overall, we argue that experiments on social media are powerful not only for studying economic issues around social media and online platforms but also for experiments studying economic behavior more broadly. Aridor, G, R Jiménez-Durán, R Levy and L Song (2025), ‘DP19861 Experiments on Social Media‘, CEPR Discussion Paper No. 19861. CEPR Press, Paris & London. https://cepr.org/publications/dp19861

Digital Commons @ USF > Office of Graduate Studies > USF Graduate Theses and Dissertations > USF Tampa Theses and Dissertations > 7745 Abhishek Bhattacharjee, University of South FloridaFollow Cross-platform influence, Social Networks Social media platforms are interconnected environments that influence each other. Information from one social media platform spreads to another. This thesis proposes a platform-independent framework to analyze information transfer across social media platforms.

This thesis uses Symbolic Transfer Entropy and Statistical Significance Test to measure influence and optimize the time window of influence between different platforms. To validate the framework, the thesis analyses the temporal activity dynamics and the information transfer across three different platforms, Reddit, Twitter and GitHub. Two data driven studies are described in this thesis. The first study finds the optimum time windows of influence between the three platforms during two different cyber attack events on cryptocurrency exchanges. It finds that specific types of activities are more influential than others, and optimum time interval changes with pre, during, and post event days. The second study applies information revealed in the first study and specifically the optimal time window to link cross-platform information cascades from Twitter and Reddit.

The case-study is a heuristic that, we show, can reduce the search space for connecting information cascades across different platforms. Two of our core faculty, Joshua Tucker and Jenny Allen, recently published a perspectives piece in Science in response to the recently published article, "Reranking partisan animosity in algorithmic social media feeds alters affective... Social media is an important source of political information, yet there is little external oversight of platforms’ ever-changing algorithms and policies. This opacity presents a major problem: Conducting a real-world experiment on the causal effects of platform features generally requires the collaboration of the platform being studied, which rarely happens, and even when it does,... On page 903 of this issue, Piccardi et al. report one possible solution to this challenge.

The authors introduce a methodological paradigm for testing the effect of social media on partisan animosity without platform collaboration by reranking users’ existing feeds using large language models (LLMs) and a browser extension. They find that changing the visibility of polarizing content can influence people’s feelings about opposing partisans. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at Ai2.

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