Allegations that TikTok shadow bans political content shape what creators post, what advertisers fund, and how regulators act, yet they are hard to adjudicate because platforms do not disclose how content is ranked. We test the claim with a dense hourly panel of 556,946 follower-normalized views across 2,753 videos from 67 accounts curated into pro and anti sides of three contested topics (U.S. immigration enforcement, Trump coverage, and Israel/Palestine). On-topic videos are identified by a multi-step classifier, and stance is taken from each account's curated side. The conventional analysis appears to answer yes. Pooling the hourly snapshots, the topic-conditional reach gap reaches p<10^-140. Analyzed at the account level, the independent unit at which we sample and assign stance, the gap disappears. Every account-level reach contrast is null after correction (BH-FDR q near 0.9). We find no evidence of moderate-to-large reach suppression on any topic. The null is informative. Account-level confidence intervals and a power analysis rule out such effects. As a design check, the same framework detects a clear asymmetry on a different outcome. Oppositional content (anti-Trump, pro-Palestine) earns more engagement per view rather than less reach (Cliff's delta = -0.51 and -0.64; q<0.03). Higher engagement does not by itself rule out suppression, but shows the design can detect effects of this magnitude. The apparent reach gap is an artifact of two factors. The first is pseudoreplication, which counts tens of thousands of autocorrelated video-hours as independent observations; the second is confounding, since the side that looks suppressed is larger and, on Israel/Palestine, posts mostly in Arabic. In this corpus, what is taken for a shadow ban is better explained by a more engaged audience than by a suppressed one. We close with what a credible visibility audit requires.
State-backed influence operations are routinely measured as high-prevalence sources of ``hate''and ``toxicity.''We argue those rates rest on a measurement error: the detectors behind them are validated to catch a broader definition inclusive of hostility or divisiveness aimed at an out-group, and so over-attribute hate to content better described as partisan or geopolitical invective. Across 25.08M tweets from seven government-attributed campaigns in the Twitter Information Operations archive (8,275 accounts), we separate hate from the other forms of divisiveness. We first validate a two-prompt LLM-based detector, matching human labels at Cohen's $\kappa=0.82$, to identify the broader hostility; we then develop an auditable rule, agreeing with an expert at $\kappa=0.52$, to further classify this content (5,457 posts) into three sub-categories. About 50.1% are identity-based attacks on people, whereas 30.4% are partisan attacks and 19.5% invective against states and their foreign policy. Reporting all of it as hate therefore overstates hate roughly twofold; only 18.7% is both identity-based and dehumanizing or inciting. Six of seven campaigns sort into three regimes that a single ``hate''rate flattens, namely identity hate (RU-op and IRA, both Russia-attributed), geopolitical invective (both Iran operations), and partisan divisiveness (both Venezuela operations). We call the shared product $manufactured divisiveness$. The line to separate these constructs itself remains unsettled: on the hardest cases three independent human experts agree only moderately (pairwise $\kappa=0.37$--$0.50$), and the best of nineteen LLM models tops out at $\kappa=0.601$ against the experts'majority. Our findings can help redefine the study of hate in the context of influence campaigns and broader online discourse.
Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content. However, existing approaches are typically tailored to platform-specific properties, such as structural affordances or linguistic conventions, which hurts generalizability across platforms. This limitation is increasingly consequential as the social media ecosystem fragments and fringe, alt-tech platforms emerge alongside mainstream ones. We propose a text-based, platform-portable methodology for measuring political partisanship in social media posts, anchored by an external news-credibility signal. Posts are embedded using a transformer-based sentence encoder and clustered into topic groups, which are labeled using the aggregated AllSides media bias scores of cited news outlets. A partisanship axis is then constructed in the embedding space as the difference between centroids of oppositely labeled clusters, and individual posts are scored by projection onto this axis. We apply the method to a corpus of approximately 1.3 million posts collected from Bluesky and Truth Social during the six months preceding the 2024 U.S. presidential election, providing the first cross-platform comparison of partisanship distributions on these two ideologically asymmetric platforms. The resulting partisanship scores correlate significantly with held-out AllSides media bias scores both in-distribution and out-of-distribution on an independent Twitter corpus, and recover within-platform partisan dynamics that platform identity alone cannot explain.
Fathima Ameen, Christopher G. Healey· arXiv.org· 0 citations
I document an ideologically asymmetric break in the pre-existing diversification trend of political discourse, emerging around late 2022, using 6 million Reddit comments from two cross-partisan forums, 2019-2025. Conservative users experienced an interruption of their prior diversification trajectory; progressive users showed no comparable change. The asymmetry is consistent across estimation strategies (ITS, DiD, RDiT, propensity-score matching) and temporal aggregations. A daily-frequency permutation test over 2,377 candidate cutoff dates shows the ChatGPT threshold produces an unremarkable estimate (49.8th percentile): the shift builds gradually instead of breaking at a single date. A continuous cumulative LLM index, tracking AI exposure across seven model releases, remains significant under a quadratic trend specification that eliminates the binary estimate. A stayer analysis narrows the mechanism: the homogenization effect disappears when the sample is restricted to authors active throughout the study period, and the stayer confidence interval excludes within-author effects even a tenth the size of the full-sample estimate. The mechanism is most parsimoniously ecological (community-level discursive convergence) rather than individual-level AI adoption, though the data cannot cleanly separate this account from concurrent secular change.
Telegram is a lightly moderated platform hosting, among others, fringe and politically extreme communities, many of which migrated after being deplatformed from mainstream social media. While prior research has mainly analyzed political debate on Telegram through in-platform text, less attention has been paid to how external media content flows into and circulates within this ecosystem. We study the circulation of YouTube videos shared in 43,000 public Telegram chats surrounding the 2024 U.S. presidential election, analyzing 686,625 English-language videos as traces of cross-platform connectivity. Using topic modeling, supervised classification, and multi-dimensional toxicity measures, we characterize which narratives are amplified, how they diffuse by analyzing reach, recirculation, persistence, and transfer time, and whether toxicity is related to amplification. We find that Telegram functions as an agenda-redistribution layer for YouTube political content. Political videos diffuse in bursty, short-lived patterns that track high-salience events. Toxicity is only weakly associated with reach; instead, more toxic content tends to persist longer within narrower thematic circuits, reinforcing segmented information environments.
Unknown authors· Proceedings of the 37th ACM...· 0 citations
A set of “stylized facts” about state-backed influence operations now circulates across journalism, policy, and the peer-reviewed literature: that they are monolithic troll armies; that they win by weaponizing moral-emotional language; that they manufacture their own virality; that they learn and optimize against feedback; and that they have become indistinguishable from ordinary users. Most rest on single-campaign studies, uncontrolled comparisons, and large-sample significance reported without baselines or multiple-comparison control, and are rarely re-tested. We assemble complete, government-attributed archives of seven state campaigns (25,076,853 tweets from 9071 accounts) with a matched organic-user baseline for five of them, and re-test all five claims under one protocol: pre-registration, Benjamini–Hochberg false-discovery control, permutation nulls, a future-reception placebo, and a takedown-snapshot decomposition. Scoped to these campaigns, every claim weakens or reverses: the operations are narratively segregated, thinly staffed production desks, not a unified army; an organic moral-contagion law fails to replicate in any of them, and a meaningless placebo predicts engagement as well; internal amplification supplies only 0.10–5.31% of top-percentile reach; the remainder is captured from an external audience whose composition—genuine organic uptake versus coordination the archive cannot see—is structurally unobservable in takedown data, a limit we state as part of the finding; behavior is scripted, with rare apparent feedback mean-reverting toward baseline; and, while their per-account language has drifted off the 2016 “troll” fingerprint, they still coordinate 7–70× more tightly than matched real users—a regularity a frozen re-test reproduces, with the language-drift and segregation patterns, across twelve further country-groups. The five verdicts do not carry equal evidentiary weight: three rest on matched-baseline contrasts, one on a placebo-gated temporal design, and one (the moral-emotional claim) on a placebo-anchored test alone, a hierarchy this paper makes explicit. The corrected picture is coherent: an industrial content factory siloed in production but coordinated in execution, whose reach it does not internally manufacture. The detectable signature has migrated from language to coordination: per-account content fingerprints age out, while cross-account coordination remains the durable, cross-national marker. Because these operations run scripts rather than optimize against feedback, an adversary that genuinely optimized—now feasible with large language models—would look measurably different from the operations studied here: a forward warning, not a present finding. The recurring lesson is methodological: on corpora of confirmed manipulation, baseline-free significance reconstructs the analyst’s expectations, and platform and policy decisions rest on those beliefs.
Deepfake technology has moved from a niche computer vision experiment to a mainstream concern for journalism, electoral processes, and public discourse. Synthetic audio, video, and images now circulate alongside genuine reporting, and the boundary between the two grows thinner each year. This article studies the rise of deepfakes, their entanglement with misinformation, and the regulatory responses unfolding across India, the European Union, and the United States. It draws on a dataset of 2,223 peer-reviewed and preprint publications in Communication and Media Studies retrieved from Dimensions for the period 2017 to 2026, supported by VOSviewer co-authorship maps and citation analysis. The piece blends qualitative discussion with a light bibliometric reading of the field. The analysis shows publication output rising sharply after 2019, climbing from 46 records in 2019 to 768 in 2025, with India contributing 111 papers and ranking second globally after the United States. Scholarly attention concentrates on misinformation, social media platforms, journalism, and democracy, while regulation, governance, and law receive less coverage. The article argues for four planks of a workable response: platform accountability, mandatory labelling, source verification, and media literacy. The findings point to a research gap on enforcement, cross-border coordination, and the position of small language communities in detection systems. The piece closes with directions for further work.
Keywords: Deepfake, Synthetic media, Misinformation, Fake news, Platform accountability.
Dr. Manindra Singh Hanspal· Naveen International Journal...· 0 citations
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