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False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations

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arxiv 2308.12497 v1 pith:XKPOG4YG submitted 2023-08-24 cs.SI cs.CYcs.LG

classification cs.SIcs.CYcs.LG
keywords informationresearchelementsfalseosnssocialbotsmanipulation
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The rapid spread of false information and persistent manipulation attacks on online social networks (OSNs), often for political, ideological, or financial gain, has affected the openness of OSNs. While researchers from various disciplines have investigated different manipulation-triggering elements of OSNs (such as understanding information diffusion on OSNs or detecting automated behavior of accounts), these works have not been consolidated to present a comprehensive overview of the interconnections among these elements. Notably, user psychology, the prevalence of bots, and their tactics in relation to false information detection have been overlooked in previous research. To address this research gap, this paper synthesizes insights from various disciplines to provide a comprehensive analysis of the manipulation landscape. By integrating the primary elements of social media manipulation (SMM), including false information, bots, and malicious campaigns, we extensively examine each SMM element. Through a systematic investigation of prior research, we identify commonalities, highlight existing gaps, and extract valuable insights in the field. Our findings underscore the urgent need for interdisciplinary research to effectively combat social media manipulations, and our systematization can guide future research efforts and assist OSN providers in ensuring the safety and integrity of their platforms.

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  1. Enhancing LLMs for Governance with Human Oversight: Evaluating and Aligning LLMs on Expert Classification of Climate Misinformation for Detecting False or Misleading Claims about Climate Change

    cs.CY 2025-01 conditional novelty 6.0 of 10

    Fine-tuned GPT-3.5-turbo agrees with expert climate coders on social media claims as often as the experts agree with each other (alpha=0.89), but the study's open-source benchmark is weakened by a flawed prompt and ra...

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