arXiv subjects

Mohammed Shahid Modi

Publications and source records attributed to Mohammed Shahid Modi.

4 recordsLinked to original sources

arXiv2025

Analysis of Collaboration in CS Prizewinning with a Nobel-Turing Comparison

In the scientific community, prizes play a pivotal role in shaping research trajectories by conferring credibility and offering financial incentives to researchers. Yet, we know little about the relationship between academic collaborations and prizewinning. By analyzing over 100 scientific prizes and the collaboration behaviors of over 5,000 prizewinners in CS, we find that prizewinners collaborate earlier and more frequently with other prizewinners than researchers who have not yet received similar recognition. Moreover, CS researchers across age groups collaborate more with prizewinners after winning their first prize, and collaborating with prizewinners after their first win increases the likelihood of the collaborator winning an award. We find that recipients of general CS prizes collaborate more than recipients of more specialized prizes, who collaborate less frequently. With Coarsened Exact Matching (CEM) and regression, we find an increase in prizewinning odds with strength of prizewinner collaboration. We examine the context of recent Nobel Prizes going to CS researchers by showing how an increasing share of Physics awards go to Physics-CS collaborations, and contrast Nobel-Turing winning author's trajectories. Our findings shed light on the relationship between prizewinning and collaboration.

cs.SI↗

arXiv2025

Limits of Large Language Models in Debating Humans

Large Language Models (LLMs) have shown remarkable promise in communicating with humans. Their potential use as artificial partners with humans in sociological experiments involving conversation is an exciting prospect. But how viable is it? Here, we rigorously test the limits of agents that debate using LLMs in a preregistered study that runs multiple debate-based opinion consensus games. Each game starts with six humans, six agents, or three humans and three agents. We found that agents can blend in and concentrate on a debate's topic better than humans, improving the productivity of all players. Yet, humans perceive agents as less convincing and confident than other humans, and several behavioral metrics of humans and agents we collected deviate measurably from each other. We observed that agents are already decent debaters, but their behavior generates a pattern distinctly different from the human-generated data.

cs.AI↗

arXiv2025

Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers

Studying public sentiment during crises is crucial for understanding how opinions and sentiments shift, resulting in polarized societies. We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis. The study period includes the pre-COVID-19 stage, the outbreak stage, and the early stage of epidemic prevention. We use Llama 3 8B, a Large Language Model, to analyze users' sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories. Analyzing sentiment shifts on Weibo provides insights into how social events and government actions influence public opinion. This study contributes to understanding the dynamics of social sentiments during health crises, fulfilling a gap in sentiment analysis for Chinese platforms. By examining these dynamics, we aim to offer valuable perspectives on digital communication's role in shaping society's responses during unprecedented global challenges.

cs.SI↗

arXiv2024

Dynamics of Ideological Biases of Social Media Users

Humanity for centuries has perfected skills of interpersonal interactions and evolved patterns that enable people to detect lies and deceiving behavior of others in face-to-face settings. Unprecedented growth of people's access to mobile phones and social media raises an important question: How does this new technology influence people's interactions and support the use of traditional patterns? In this article, we answer this question for homophily-driven patterns in social media. In our previous studies, we found that, on a university campus, changes in student opinions were driven by the desire to hold popular opinions. Here, we demonstrate that the evolution of online platform-wide opinion groups is driven by the same desire. We focus on two social media: Twitter and Parler, on which we tracked the political biases of their users. On Parler, an initially stable group of Right-biased users evolved into a permanent Right-leaning echo chamber dominating weaker, transient groups of members with opposing political biases. In contrast, on Twitter, the initial presence of two large opposing bias groups led to the evolution of a bimodal bias distribution, with a high degree of polarization. We capture the movement of users from the initial to final bias groups during the tracking period. We also show that user choices are influenced by side-effects of homophily. Users entering the platform attempt to find a sufficiently large group whose members hold political biases within the range sufficiently close to their own. If successful, they stabilize their biases and become permanent members of the group. Otherwise, they leave the platform. We believe that the dynamics of users' behavior uncovered in this article create a foundation for technical solutions supporting social groups on social media and socially aware networks.

cs.SI↗