Why we think this paper is great for you:
This paper directly addresses the study and prediction of social movements, which is a core area of interest for you. It offers tools and insights into understanding how these movements evolve and impact global goals.

Abstract
Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025 that will be publicly released for research purposes upon publication of this paper.

AI Summary

• DEEP is a novel transformer-based multivariate time series model that jointly forecasts future discourse volume and the intensity of 28 discrete emotions across Reddit and news for social movements. [3]
• The system provides probabilistic forecasts with uncertainty estimates (Student-t distributions), offering critical support for editorial planning and strategic decision-making in journalism. [3]
• DEEP exhibits platform-specific predictive dynamics: it achieves high precision on news for short-horizon forecasts, while its predictions for Reddit improve significantly with longer horizons, indicating different temporal patterns of discourse evolution. [3]
• DEEP's development was guided by extensive feedback from over 20 journalists, ensuring its utility in identifying emerging trends and informing timely editorial decisions before mainstream attention. [3]
• The system demonstrates strong performance in predicting directional changes in emotions, which is more critical for journalists than exact magnitude matching, as it signals shifts in public interest. [3]
• DEEP (Discourse Evolution Engine for Predictions about Social Movements): The transformer-based multivariate time series model specifically developed to predict the volume of future articles/posts and the emotions expressed within social movement discourse. [3]
• A multi-layer data extraction methodology is employed to balance precision and recall, capturing both direct mentions of a social movement and semantically related discussions through keyword co-occurrence thresholds. [2]
• SMART (Social Media Analysis & Reasoning Tool): A broader system designed to track social movements related to the UN's Sustainable Development Goals (SDGs), with DEEP as a core component. [2]
• The paper introduces a novel, publicly-releasable longitudinal dataset for the #MeToo movement, comprising 433,016 Reddit posts and 121,849 news articles, which is a valuable resource for future research. [1]
• Discourse Evolution Prediction Problem: The task of estimating the future discourse state (St+Δ) given a historical trajectory (Ht) and anticipated key events (Kt:t+Δ). [1]

Why we think this paper is great for you:
You will find this paper highly relevant as it explores political communication and public engagement. It offers insights into the dynamics of political feedback mechanisms and how sentiment influences interaction.

Abstract
We investigate feedback mechanisms in political communication by testing whether politicians adapt the sentiment of their messages in response to public engagement. Using over 1.5 million tweets from Members of Parliament in the United Kingdom, Spain, and Greece during 2021, we identify sentiment dynamics through a simple yet interpretable linear model. The analysis reveals a closed-loop behavior: engagement with positive and negative messages influences the sentiment of subsequent posts. Moreover, the learned coefficients highlight systematic differences across political roles: opposition members are more reactive to negative engagement, whereas government officials respond more to positive signals. These results provide a quantitative, control-oriented view of behavioral adaptation in online politics, showing how feedback principles can explain the self-reinforcing dynamics that emerge in social media discourse.

Why we think this paper is great for you:
This paper directly investigates democratic processes and standards within elections, specifically focusing on i-voting systems. It aligns well with your interest in democratic institutions and their practical application.

Abstract
The shift towards increased remote work and digital communication, driven by recent global developments, has led to the widespread adoption of i-voting systems, including in academic institutions. This paper critically evaluates the use of i-voting platforms for elections to academic senates at Czech public universities, focusing on the democratic and technical challenges they present. A total of 18 out of 26 Czech public universities have implemented remote electronic voting for these elections. Yet, the systems often lack the necessary transparency, raising significant concerns regarding their adherence to democratic norms, such as election security, voter privacy, and the integrity of the process. Through interviews with system developers and administrators, along with a survey of potential voters, the study underscores the critical need for transparency. Without it, a comprehensive assessment of the technical standards and the overall legitimacy of the i-voting systems remains unattainable, potentially undermining the credibility of the electoral outcomes.

Why we think this paper is great for you:
The title "Making Democracy Work" directly resonates with your interests in democratic systems. While technical, it offers a perspective on robust, decentralized system design that mirrors ideals of democratic functionality.

Abstract
Classical state-machine replication protocols, such as Paxos, rely on a distinguished leader process to order commands. Unfortunately, this approach makes the leader a single point of failure and increases the latency for clients that are not co-located with it. As a response to these drawbacks, Egalitarian Paxos introduced an alternative, leaderless approach, that allows replicas to order commands collaboratively. Not relying on a single leader allows the protocol to maintain non-zero throughput with up to $f$ crashes of any processes out of a total of $n = 2f+1$. The protocol furthermore allows any process to execute a command $c$ fast, in $2$ message delays, provided no more than $e = \lceil\frac{f+1}{2}\rceil$ other processes fail, and all concurrently submitted commands commute with $c$; the latter condition is often satisfied in practical systems. Egalitarian Paxos has served as a foundation for many other replication protocols. But unfortunately, the protocol is very complex, ambiguously specified and suffers from nontrivial bugs. In this paper, we present EPaxos* -- a simpler and correct variant of Egalitarian Paxos. Our key technical contribution is a simpler failure-recovery algorithm, which we have rigorously proved correct. Our protocol also generalizes Egalitarian Paxos to cover the whole spectrum of failure thresholds $f$ and $e$ such that $n \ge \max\{2e+f-1, 2f+1\}$ -- the number of processes that we show to be optimal.

Why we think this paper is great for you:
This paper explores predicting sociodemographics from mobility data, which can provide foundational insights for understanding populations. This data can be highly relevant to political economy and social dynamics.

Abstract
Inferring sociodemographic attributes from mobility data could help transportation planners better leverage passively collected datasets, but this task remains difficult due to weak and inconsistent relationships between mobility patterns and sociodemographic traits, as well as limited generalization across contexts. We address these challenges from three angles. First, to improve predictive accuracy while retaining interpretability, we introduce a behaviorally grounded set of higher-order mobility descriptors based on directed mobility graphs. These features capture structured patterns in trip sequences, travel modes, and social co-travel, and significantly improve prediction of age, gender, income, and household structure over baselines features. Second, we introduce metrics and visual diagnostic tools that encourage evenness between model confidence and accuracy, enabling planners to quantify uncertainty. Third, to improve generalization and sample efficiency, we develop a multitask learning framework that jointly predicts multiple sociodemographic attributes from a shared representation. This approach outperforms single-task models, particularly when training data are limited or when applying models across different time periods (i.e., when the test set distribution differs from the training set).

Why we think this paper is great for you:
This paper delves into online optimization and sequential decision-making under combinatorial constraints. While abstract, such models can sometimes inform strategic choices within political or economic systems.

Abstract
The Matroid Secretary Problem is a central question in online optimization, modeling sequential decision-making under combinatorial constraints. We introduce a bipartite graph framework that unifies and extends several known formulations, including the bipartite matching, matroid intersection, and random-order matroid secretary problems. In this model, elements form a bipartite graph between agents and items, and the objective is to select a matching that satisfies feasibility constraints on both sides, given by two independence systems. We study the free-order setting, where the algorithm may adaptively choose the next element to reveal. For $k$-matroid intersection, we leverage a core lemma by (Feldman, Svensson and Zenklusen, 2022) to design an $\Omega(1/k^2)$-competitive algorithm, extending known results for single matroids. Building on this, we identify the structural property underlying our approach and introduce $k$-growth systems. We establish a generalized core lemma for $k$-growth systems, showing that a suitably defined set of critical elements retains a $\Omega(1/k^2)$ fraction of the optimal weight. Using this lemma, we extend our $\Omega(1/k^2)$-competitive algorithm to $k$-growth systems for the edge-arrival model. We then study the agent-arrival model, which presents unique challenges to our framework. We extend the core lemma to this model and then apply it to obtain an $\Omega(\beta/k^2)$-competitive algorithm for $k$-growth systems, where $\beta$ denotes the competitiveness of a special type of order-oblivious algorithm for the item-side constraint. Finally, we relax the matching assumption and extend our results to the case of multiple item selection, where agents have individual independence systems coupled by a global item-side constraint. We obtain constant-competitive algorithms for fundamental cases such as partition matroids and $k$-matching constraints.

Why we think this paper is great for you:
This paper's focus on tracking social movements through social media analysis offers a practical approach to understanding contemporary activism and political dynamics. It provides valuable insights into the evolution of political movements.

Abstract
Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025 that will be publicly released for research purposes upon publication of this paper.

AI Summary

• DEEP is a novel transformer-based multivariate time series model that jointly forecasts future discourse volume and the intensity of 28 discrete emotions across Reddit and news for social movements. [3]
• The system provides probabilistic forecasts with uncertainty estimates (Student-t distributions), offering critical support for editorial planning and strategic decision-making in journalism. [3]
• DEEP exhibits platform-specific predictive dynamics: it achieves high precision on news for short-horizon forecasts, while its predictions for Reddit improve significantly with longer horizons, indicating different temporal patterns of discourse evolution. [3]
• DEEP's development was guided by extensive feedback from over 20 journalists, ensuring its utility in identifying emerging trends and informing timely editorial decisions before mainstream attention. [3]
• The system demonstrates strong performance in predicting directional changes in emotions, which is more critical for journalists than exact magnitude matching, as it signals shifts in public interest. [3]
• DEEP (Discourse Evolution Engine for Predictions about Social Movements): The transformer-based multivariate time series model specifically developed to predict the volume of future articles/posts and the emotions expressed within social movement discourse. [3]
• A multi-layer data extraction methodology is employed to balance precision and recall, capturing both direct mentions of a social movement and semantically related discussions through keyword co-occurrence thresholds. [2]
• SMART (Social Media Analysis & Reasoning Tool): A broader system designed to track social movements related to the UN's Sustainable Development Goals (SDGs), with DEEP as a core component. [2]
• The paper introduces a novel, publicly-releasable longitudinal dataset for the #MeToo movement, comprising 433,016 Reddit posts and 121,849 news articles, which is a valuable resource for future research. [1]
• Discourse Evolution Prediction Problem: The task of estimating the future discourse state (St+Δ) given a historical trajectory (Ht) and anticipated key events (Kt:t+Δ). [1]