Online social networks have transformed the ways in which political mobilization messages are disseminated, raising new questions about how peer influence operates at scale. Building on the landmark 61-million-person Facebook experiment \citep{bond201261}, we develop an agent-based simulation framework that integrates real U.S. Census demographic distributions, authentic Twitter network topology, and heterogeneous large language model (LLM) agents to examine the effect of mobilization messages on voter turnout. Each simulated agent is assigned demographic attributes, a personal political stance, and an LLM variant (\texttt{GPT-4.1}, \texttt{GPT-4.1-Mini}, or \texttt{GPT-4.1-Nano}) reflecting its political sophistication. Agents interact over realistic social network structures, receiving personalized feeds and dynamically updating their engagement behaviors and voting intentions. Experimental conditions replicate the informational and social mobilization treatments of the original Facebook study. Across scenarios, the simulator reproduces qualitative patterns observed in field experiments, including stronger mobilization effects under social message treatments and measurable peer spillovers. Our framework provides a controlled, reproducible environment for testing counterfactual designs and sensitivity analyses in political mobilization research, offering a bridge between high-validity field experiments and flexible computational modeling.\footnote{Code and data available at https://github.com/CausalMP/LLM-SocioPol}

Abstract--The 2024 July Revolution in Bangladesh represents a landmark event in the study of civil resistance: a successful, student-led civilian uprising that overthrew a long-standing authoritarian regime despite facing brutal state repression. This study investigates the central paradox of its success: how state violence, intended to quell dissent, ultimately fueled the movement's victory. We employ a mixed-methods approach. First, we develop a qualitative narrative of the conflict's timeline to generate specific, testable hypotheses. Then, using a disaggregated, event-level dataset, we employ a multi-method quantitative analysis to dissect the complex relationship between repression and mobilisation. We provide a framework to analyse explosive modern uprisings like the July Revolution. Initial pooled regression models highlight the crucial role of protest momentum (measured by a feedback loop effect) in sustaining the movement. T o isolate causal effects, we specify a Two-Way Fixed Effects panel model, which provides robust evidence for a direct and statistically significant local suppression backfire effect. Our V ector Autoregression (V AR) analysis provides clear visual evidence of an immediate, nationwide mobilisation in response to increased lethal violence. We further demonstrate that this effect was non-linear . A structural break analysis reveals that the backfire dynamic was statistically insignificant in the conflict's early phase but was triggered by the catalytic moral shock of the first wave of lethal violence, and its visuals circulated around July 16th. We conclude that the July Revolution was driven by a contingent, non-linear backfire, triggered by specific catalytic moral shocks and accelerated by the viral reaction to the visual spectacle of state brutality. N August 2024, the fifteen-year rule of Prime Minister Sheikh Hasina of Bangladesh came to a sudden and dramatic end. After weeks of escalating nationwide protests, she resigned from her post and fled the country. These authors contributed equally to this work. Saiful Bari Siddiqui is a Senior Lecturer at the Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh (e-mail: saiful.bari@bracu.ac.bd). Anupam Debashis Roy is a PhD candidate at the Department of Sociology, University of Oxford, Oxford, United Kingdom (e-mail: anu-pam.roy@sant.ox.ac.uk). In a matter of weeks, this initial spark grew into a nationwide fire, as hundreds of thousands of ordinary citizens joined the students, bringing the country to a standstill and achieving a political transformation that had seemed unthinkable just a month earlier.

We study how participation in collective action is articulated in podcast discussions, using the Black Lives Matter (BLM) movement as a case study. While research on collective action discourse has primarily focused on text-based content, this study takes a first step toward analyzing audio formats by using podcast transcripts. Using the Structured Podcast Research Corpus (SPoRC), we investigated spoken language expressions of participation in collective action, categorized as problem-solution, call-to-action, intention, and execution. We identified podcast episodes discussing racial justice after important BLM-related events in May and June of 2020, and extracted participatory statements using a layered framework adapted from prior work on social media. We examined the emotional dimensions of these statements, detecting eight key emotions and their association with varying stages of activism. We found that emotional profiles vary by stage, with different positive emotions standing out during calls-to-action, intention, and execution. We detected negative associations between collective action and negative emotions, contrary to theoretical expectations. Our work contributes to a better understanding of how activism is expressed in spoken digital discourse and how emotional framing may depend on the format of the discussion.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Sirens wailed across Russia and TV stations interrupted regular programming to broadcast warnings Wednesday as part of sweeping drills intended to test the readiness of the country's emergency responders amid the fighting in Ukraine. The exercise that started Tuesday follows Ukrainian drone attacks on Moscow and other cities. As the readiness drill went on, the Russian Defense Ministry said air defenses shot down 31 Ukrainian drones over border regions early Wednesday. The readiness of the public warning system is being tested!

Senior foreign affairs correspondent Greg Palkot reports the latest. Russia on Tuesday is claiming that so far this year, more than 335,000 people have signed up to fight in its military and volunteer units, although a further deployment to Ukraine is not coming, a report says. Reuters, citing Russian state television, quoted Defense Minister Sergei Shoigu telling top generals that there are "no plans for an additional mobilization" and that "the armed forces have the necessary number of military personnel to conduct the special military operation" in Ukraine. "Since the start of the year, more than 335,000 people have entered military service under contract and in volunteer formations," Shoigu reportedly added. "In September alone, more than 50,000 citizens signed contracts."

Two studies tested the hypothesis that a Large Language Model (LLM) can be used to model psychological change following exposure to influential input. The first study tested a generic mode of influence - the Illusory Truth Effect (ITE) - where earlier exposure to a statement (through, for example, rating its interest) boosts a later truthfulness test rating. Data was collected from 1000 human participants using an online experiment, and 1000 simulated participants using engineered prompts and LLM completion. 64 ratings per participant were collected, using all exposure-test combinations of the attributes: truth, interest, sentiment and importance. The results for human participants reconfirmed the ITE, and demonstrated an absence of effect for attributes other than truth, and when the same attribute is used for exposure and test. The same pattern of effects was found for LLM-simulated participants. The second study concerns a specific mode of influence - populist framing of news to increase its persuasion and political mobilization. Data from LLM-simulated participants was collected and compared to previously published data from a 15-country experiment on 7286 human participants. Several effects previously demonstrated from the human study were replicated by the simulated study, including effects that surprised the authors of the human study by contradicting their theoretical expectations (anti-immigrant framing of news decreases its persuasion and mobilization); but some significant relationships found in human data (modulation of the effectiveness of populist framing according to relative deprivation of the participant) were not present in the LLM data. Together the two studies support the view that LLMs have potential to act as models of the effect of influence.

Russia's troops fought Thursday to regain lost ground in areas of Ukraine that Russian President Vladimir Putin has illegally annexed while Moscow tried to pound the invaded country into submission with more missile and drone attacks on critical infrastructure. Russian forces attacked Ukrainian positions near Bilohorivka, a village in the Luhansk region of eastern Ukraine. In the neighboring Donetsk region, fighting raged near the city of Bakhmut. Kremlin-backed separatists have controlled parts of both regions for 8½ years. Putin declared martial law in Luhansk, Donetsk and southern Ukraine's Zaporizhzhia and Kherson regions on Wednesday in an attempt to assert Russian authority in the annexed areas following a string of battlefield setbacks and a troubled troop mobilization.

Hallam is an Associate Professor in the History Programme and in the School of Biological Sciences at Nanyang Technological University in Singapore. Daniel is an external PhD candidate at eLaw -- Center for Law and Digital Technologies, Leiden University, the Netherlands. This essay is part of our ongoing "AI Lexicon" project, a call for contributions to generate alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI. Much of the history, meaning, and imagination of AI is discussed in relation to the West, often against a backdrop of cybernetics, "AI winters," and Terminator androids. These narratives inform how we understand the risks and "social good" of AI.

With the rise of the Internet and social media, social mobilization - large-scale mobilization manpower for scientific, social, and political activities through crowdsourcing - has become a widespread practice. Despite the success, social mobilization is not without its limitations. Local trapping of diffusion and the dependence on highly connected individuals to mobilize people in distance locations affect the effectiveness of social mobilization. Furthermore, as empirical studies on people's responses to various social mobilization approaches are lacking, it is a significant challenge for artificial intelligence (AI) researchers to design effective and efficient decision support mechanisms to help manage this emerging phenomenon. In my thesis, I conduct large-scale empirical studies to help the AI research community establish baseline personal variabilities in different people's response patterns to social mobilization approaches. Based on the collected dataset, I will further propose computational algorithmic crowdsourcing mechanisms which leverage the empirical evidence to improve the effectiveness and efficiency of social mobilization, towards achieving superlinear productivity. Throughout this process, I will also incorporate human factors into the computational models to benefit social mobilization efforts.