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Once united in support of President Trump, right-wing influencers are embroiled in feuds and losing viewers, creating a problem for Republicans in the midterm elections
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,
Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.5, to emulate the authorial signatures of prominent literary and political figures: Walt Whitman, William Wordsworth, Donald Trump, and Barack Obama. Utilizing a zero-shot prompting framework with strict thematic alignment, we generated synthetic corpora evaluated through a complementary framework combining transformer-based classification (BERT) and interpretable machine learning (XGBoost). Our methodology integrates Linguistic Inquiry and Word Count (LIWC) markers, perplexity, and readability indices to assess the divergence between AI-generated and human-authored text. Results demonstrate that AI-generated mimicry remains highly detectable, with XGBoost models trained on a restricted set of eight stylometric features achieving accuracy comparable to high-dimensional neural classifiers. Feature importance analyses identify perplexity as the primary discriminative metric, revealing a significant divergence in the stochastic regularity of AI outputs compared to the
Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many Natural Language Processing tasks, such as hateful speech detection or sentiment analysis. Surprisingly, the evaluation of this kind of bias in stance detection methods has been largely overlooked by the community. Stance Detection involves labeling a statement as being against, in favor, or neutral towards a specific target and is among the most sensitive NLP tasks, as it often relates to political leanings. In this paper, we focus on the bias of Large Language Models when performing stance detection in a zero-shot setting. We automatically annotate posts in pre-existing stance detection datasets with two attributes: dialect or vernacular of a specific group and text complexity/readability, to investigate whether these attributes influence the model's stance detection decisions. Our results show that LLMs exhibit significant stereotypes in stance detection tasks, such as incorrectly associating pro-marijuana views with low text complexity and African American dialect with opposition to Donald Trump.
Automated social media accounts, known as bots, have been shown to spread disinformation and manipulate online discussions. We study the behavior of retweet bots on Twitter during the first impeachment of U.S. President Donald Trump. We collect over 67.7 million impeachment related tweets from 3.6 million users, along with their 53.6 million edge follower network. We find although bots represent 1% of all users, they generate over 31% of all impeachment related tweets. We also find bots share more disinformation, but use less toxic language than other users. Among supporters of the Qanon conspiracy theory, a popular disinformation campaign, bots have a prevalence near 10%. The follower network of Qanon supporters exhibits a hierarchical structure, with bots acting as central hubs surrounded by isolated humans. We quantify bot impact using the generalized harmonic influence centrality measure. We find there are a greater number of pro-Trump bots, but on a per bot basis, anti-Trump and pro-Trump bots have similar impact, while Qanon bots have less impact. This lower impact is due to the homophily of the Qanon follower network, suggesting this disinformation is spread mostly within on
As yet another alternative social network, Gettr positions itself as the "marketplace of ideas" where users should expect the truth to emerge without any administrative censorship. We looked deep inside the platform by analyzing it's structure, a sample of 6.8 million posts, and the responses from a sample of 124 Gettr users we interviewed to see if this actually is the case. Administratively, Gettr makes a deliberate attempt to stifle any external evaluation of the platform as collecting data is marred with unpredictable and abrupt changes in their API. Content-wise, Gettr notably hosts pro-Trump content mixed with conspiracy theories and attacks on the perceived "left." It's social network structure is asymmetric and centered around prominent right-thought leaders, which is characteristic for all alt-platforms. While right-leaning users joined Gettr as a result of a perceived freedom of speech infringement by the mainstream platforms, left-leaning users followed them in numbers as to "keep up with the misinformation." We contextualize these findings by looking into the Gettr's user interface design to provide a comprehensive insight into the incentive structure for joining and co
Widespread conspiracy theories may significantly impact our society. This paper focuses on the QAnon conspiracy theory, a consequential conspiracy theory that started on and disseminated successfully through social media. Our work characterizes how Reddit users who have participated in QAnon-focused subreddits engage in activities on the platform, especially outside their own communities. Using a large-scale Reddit moderation action against QAnon-related activities in 2018 as the source, we identified 13,000 users active in the early QAnon communities. We collected the 2.1 million submissions and 10.8 million comments posted by these users across all of Reddit from October 2016 to January 2021. The majority of these users were only active after the emergence of the QAnon Conspiracy theory and decreased in activity after Reddit's 2018 QAnon ban. A qualitative analysis of a sample of 915 subreddits where the "QAnon-enthusiastic" users were especially active shows that they participated in a diverse range of subreddits, often of unrelated topics to QAnon. However, most of the users' submissions were concentrated in subreddits that have sympathetic attitudes towards the conspiracy theo
Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused "trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's information ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence automated detection is not a straightforward task. Using the Hawkes Processe
A disputed exam, an unreliable detector, and one very late Apple Pages file
Scientists have created twisted laser beams that interact differently with right-handed and left-handed molecules, revealing their identity through the fragments they produce。 The approach could provide a faster, simpler, and more sensitive way to analyze important molecules used in chemistry and pharmaceuticals
Researchers are applying evolutionary theory to cancer by changing treatments before tumors have time to develop resistance。 Mathematical models suggest that rapid, carefully timed switches between multiple therapies could improve cure rates
Scientists have created a programmable optical chip that can slow light on demand, giving engineers far greater control over how optical signals propagate through a circuit。 The technology could provide the delays, synchronization, and buffering functions needed to make light-based computing more practical。 A single chip could eventually perform se
Researchers have created self-destructing living plastic that uses engineered bacteria to completely break itself down when activated。 The material degrades in just six days without creating microplastics, offering a potential new solution for single-use plastic waste
China’s Tianwen-1 orbiter captured a faint but remarkable view of 3I/ATLAS as the interstellar comet raced past Mars。 The images reveal its tail and glowing coma, offering scientists a glimpse of material born beyond our solar system
Scientists are testing two promising ways to destroy PFAS, the stubborn “forever chemicals” that can accumulate in water and resist normal treatment。 One method uses collapsing vapor bubbles to generate extreme heat and reactive molecules, while the other uses cold plasma and rising gas bubbles to pull PFAS to the surface and break them apart
Scientists have uncovered new evidence that Venus may still be tearing itself apart from within。 Advanced 3D simulations indicate that some of the planet's giant rift valleys formed relatively recently and could still be expanding。 The results suggest Venus has a far more active interior than researchers once believed, challenging the long-held vie
A distant Sun-like star appears to have devoured one of its planets, leaving behind a surprising chemical fingerprint。 Researchers found an unusually high concentration of lithium, a strong sign that planetary material was mixed into the star。 Careful comparisons with dozens of similar stars confirmed the signal is highly unusual, and scientists th