Life and Death of Fakes: on Data Persistence for Manipulative Social Media Content
Authors: Olga Uryupina (Università di Trento, 2024)
Topic: Fact Checking and Fake News Detection

Abstract
This work presents an in-depth investigation of the data decay for publicly fact-checked online content. We monitor compromised posts on major social media platforms (Facebook, Instagram, Twitter, TikTok) for one year, tracking the changes in their visibility and availability. We show that data persistence is an important issue for manipulative content, on a larger scale than previously reported for online content in general. Our findings also suggest a (much) higher data decay rate for the platforms suffering most from online disinformation, indicating an important area for data collection/preservation.

Introduction
Manipulative online content is rapidly becoming a more and more pervasive issue for the modern society: by deliberately biasing our information flow, unscrupulous content writers can and do affect our emotional state, beliefs, reasoning and both online and offline behaviour. It is therefore not surprising that this has become a central issue for various stakeholders, from journalists and fact-checkers to NLP researchers both in academia and in the industry. Given the current rapid growth in data-driven studies of manipulative content, it is essential to have a reliable overview of data persistence issues in this specific domain: compromised content is often very dynamic and changes or becomes unavailable over time, raising reproducibility concerns, From the readers' perspective, the visibility of compromised content over time affects directly its impact: a removed or strongly downgraded document is unlikely to be read/recovered and cannot be used to promote or support other fakes. From the research and development perspective, data persistence is crucial for benchmarking, ensuring fair comparison between models as well as even simply providing them with high-quality real-life training and testing examples. Starting from already a decade ago, NLP benchmarking campaign studies AUTHOR report data persistence issues for online content, as used in various shared tasks, reporting around 10% of entries missing compared to the original dataset (gold standard). These shared tasks, however, are based almost exclusively on Twitter and do not focus specifically on compromised content. We believe that a large proportion of manipulative content is created on purpose by professional copywriters who might have different goals and motivations to keep their texts online (e.g., for click-bait purposes) or remove them (e.g., to reduce the reputation loss from being exposed as unreliable). Our work focuses specifically on the lifespan of fact-checked compromised content. We go beyond the naive binary present vs. removed view, studying more nuanced cases as well. In particular, we track compromised online posts over time for the appearance of explicit platform-specific reliability labels (e.g. "out of context"), obfuscation (the common situation when the online content is -- fully or partially -- rendered either very blurred or as a black/white box, with a message raising awareness of its limited reliability; this content, however, is still accessible to the user upon an extra click), and author-generated edits, as well as complete content removal. More specifically, we address the following research questions: \item[RQ1:] How persistent is the compromised content? How does its visibility and availability change over time? \item[RQ2:] What is the typical timeline for interaction between the content generators and fact-checkers? How -- if at all -- do content writers alter their posts after being exposed as problematic by fact checkers? \item[RQ3:] Are the trends different across platforms? To this end, we analyze two datasets (in English) of social media documents, fact-checked by PolitiFact.) is an independent journalistic agency and one of the most experienced fact-checking organizations, providing detailed analytics for non-transparent online content since 2007.

Conclusion
This paper aims at an in-depth analysis of data persistence for publicly fact-checked online content. After one year of monitoring thoroughly online posts fact-checked by PolitiFact, we have observed the following findings. First, the data persistence is a crucial and underrated issue for compromised content, with considerable decay rates. Second, the decay trends differ across platforms, with Facebook, TikTok and Instagram showing much less data persistance. Third, the decay starts immediately, with 12% of the compromised posts getting deleted at (or before) the publication of the PolitiFact report and 20% becoming unavailable within a week. This suggests an urgent need for a concentrated effort on timely collecting real-life fakes if we want to go beyond synthetic or simplistic datasets and train impactful fact-checking models. In the future, we want to analyze further aspects of the decay issues for the compromised content. Thus, we plan to add more fact-checking outlets beyond PolitiFact to see if there are any effects due to the report itself. Second, we plan to study in more detail the difference in online behaviour (content removal) between anonymous users, non-anonymous users and public figures. Finally, we plan to expand our research on interaction between content writers and fact-checkers ("editing"). We thank the Autonomous Province of Trento for the financial support of our project via the AI@TN initiative.