Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms

Social media is often criticized for amplifying toxic discourse and discouraging constructive conversations. But designing social media platforms to promote better conversations is inherently challenging. This paper asks whether simulating social media through a combination of Large Language Models (LLM) and Agent-Based Modeling can help researchers study how different news feed algorithms shape the quality of online conversations. We create realistic personas using data from the American National Election Study to populate simulated social media platforms. Next, we prompt the agents to read and share news articles - and like or comment upon each other's messages - within three platforms that use different news feed algorithms. In the first platform, users see the most liked and commented posts from users whom they follow. In the second, they see posts from all users - even those outside their own network. The third platform employs a novel "bridging" algorithm that highlights posts that are liked by people with opposing political views. We find this bridging algorithm promotes more constructive, non-toxic, conversation across political divides than the other two models. Though further research is needed to evaluate these findings, we argue that LLMs hold considerable potential to improve simulation research on social media and many other complex social settings

Sur les frontières de la reconnaissance

Faisant appel aux études récentes portant sur la reconnaissance et l’identité sociale, nous analysons les changements dans la catégorisation de l’identité collective des groupes stigmatisés en Israël, en Irlande du Nord, au Québec et au Brésil. Alors que la littérature sur la reconnaissance tend à présumer une opposition nette entre « nous » et « eux », l’analyse de la littérature empirique démontre la complexification et la multiplication des catégories d’identité. Dans les quatre cas nous avons observé le processus de reconnaissance, en explorant les transformations de la signification des frontières internes et externes de l’identité collective pour ses membres ainsi que pour ceux qui lui sont extérieurs. Nous soutenons que la nature conditionnelle de la reconnaissance devrait conduire les chercheurs à considérer non seulement les composantes normatives du conflit ethnique mais aussi, en leur accordant une importance particulière, le langage et la catégorisation qui fondent ce type de débat.On the Boundaries of Recognition. Internal and External Categories of Collective Identity.Drawing upon recent advances in the study of recognition and social identity, we trace changes in the categorization of collective identity among stigmatized groups in Israel, Northern reland, Québec, and Brazil. While the recognition literature commonly assumes an opposition between « Us » and « Them », a review of these empirical cases illustrates the full complexity of identity categories in each of the four cases. We focus on the process of recognition in each case while highlighting the significance of internal and external boundaries of collective identity. We argue that the contingent nature of recognition should lead scholars to consider not only the normative components of ethnic conflict, but more importantly the language and categories which form the basis for such debates.En las fronteras del reconocimiento. Las categorías internas y externas de la identidad colectiva.Fundándonos en estudios recientes sobre el reconocimiento y la identidad social analizamos los cambios de categorización de la identidad colectiva de grupos estigmatizados en Israel, en Irlanda del Norte, en el Québec canadiense y en Brasil. Cuando la literatura sobre reconocimiento presume una oposición neta entre “nosotros” y “ellos” el análisis de los estudios empíricos demuestra la complicación y la multiplicación de las categorías de identidad. En los cuatro casos que hemos observado el proceso de reconocimiento, explorando las transfor­maciones la significación de la las fronteras internas y externas de la identidad colectiva para sus miembros como para los que son exteriores a ella. Consideramos que la naturaleza condicional del reconocimiento debe llevar a los investigadores a analizar no solo a los componentes normativos des conflicto étnico sino también, dándoles una importancia particular, el lenguaje y la categorización que fundan este tipo de debate

AI Chat Assistants can Improve Conversations about Divisive Topics

A rapidly increasing amount of human conversation occurs online. But divisiveness and conflict can fester in text-based interactions on social media platforms, in messaging apps, and on other digital forums. Such toxicity increases polarization and, importantly, corrodes the capacity of diverse societies to develop efficient solutions to complex social problems that impact everyone. Scholars and civil society groups promote interventions that can make interpersonal conversations less divisive or more productive in offline settings, but scaling these efforts to the amount of discourse that occurs online is extremely challenging. We present results of a large-scale experiment that demonstrates how online conversations about divisive topics can be improved with artificial intelligence tools. Specifically, we employ a large language model to make real-time, evidence-based recommendations intended to improve participants' perception of feeling understood in conversations. We find that these interventions improve the reported quality of the conversation, reduce political divisiveness, and improve the tone, without systematically changing the content of the conversation or moving people's policy attitudes. These findings have important implications for future research on social media, political deliberation, and the growing community of scholars interested in the place of artificial intelligence within computational social science

An online experiment during the 2020 US-Iran crisis shows that exposure to common enemies can increase political polarization

A longstanding theory indicates that the threat of a common enemy can mitigate conflict between members of rival groups. We tested this hypothesis in a pre-registered experiment where 1670 Republicans and Democrats in the United States were asked to complete an online social learning task with a bot that was labeled as a member of the opposing party. Prior to this task, we exposed respondents to primes about (a) a common enemy (involving Iran and Russia); (b) a patriotic event; or (c) a neutral, apolitical prime. Though we observed no significant differences in the behavior of Democrats as a result of priming, we found that Republicans-and particularly those with very strong conservative views-were significantly less likely to learn from Democrats when primed about a common enemy. Because our study was in the field during the 2020 Iran Crisis, we were able to further evaluate this finding via a natural experiment-Republicans who participated in our study after the crisis were even less influenced by the beliefs of Democrats than those Republicans who participated before this event. These findings indicate common enemies may not reduce inter-group conflict in highly polarized societies, and contribute to a growing number of studies that find evidence of asymmetric political polarization in the United States. We conclude by discussing the implications of these findings for research in social psychology, political conflict, and the rapidly expanding field of computational social science

REFORMS: Reporting Standards for Machine Learning Based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility

The neutron and its role in cosmology and particle physics

Experiments with cold and ultracold neutrons have reached a level of precision such that problems far beyond the scale of the present Standard Model of particle physics become accessible to experimental investigation. Due to the close links between particle physics and cosmology, these studies also permit a deep look into the very first instances of our universe. First addressed in this article, both in theory and experiment, is the problem of baryogenesis ... The question how baryogenesis could have happened is open to experimental tests, and it turns out that this problem can be curbed by the very stringent limits on an electric dipole moment of the neutron, a quantity that also has deep implications for particle physics. Then we discuss the recent spectacular observation of neutron quantization in the earth's gravitational field and of resonance transitions between such gravitational energy states. These measurements, together with new evaluations of neutron scattering data, set new constraints on deviations from Newton's gravitational law at the picometer scale. Such deviations are predicted in modern theories with extra-dimensions that propose unification of the Planck scale with the scale of the Standard Model ... Another main topic is the weak-interaction parameters in various fields of physics and astrophysics that must all be derived from measured neutron decay data. Up to now, about 10 different neutron decay observables have been measured, much more than needed in the electroweak Standard Model. This allows various precise tests for new physics beyond the Standard Model, competing with or surpassing similar tests at high-energy. The review ends with a discussion of neutron and nuclear data required in the synthesis of the elements during the "first three minutes" and later on in stellar nucleosynthesis.Comment: 91 pages, 30 figures, accepted by Reviews of Modern Physic

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Bridging Boundaries: The Equalization Strategies of Stigmatized Ethno-racial Groups Compared. CES Working Papers No. 154, 2008

This article offers a framework for analyzing variations in how members of stigmatized ethnoracial groups establish equivalence with dominant groups through the comparative study of “equalization strategies.” Whereas extant scholarship on anti-racism has focused on the struggle of social movements against institutional and political exclusion and social justice, we are concerned with the “everyday” anti-racist strategies deployed by members of stigmatized groups. We seek to compare how these strategies vary according to the permeability of inter-group boundaries. The first section defines our research problem and the second section locates our agenda within the current literature. The third section sketches an empirical context for the comparative analysis of equalization strategies across four cases: Palestinian citizens of Israel, Catholics in Northern Ireland, blacks in Brazil, and Québécois in Canada. Whereas the first two cases are examples of ethnic conflict where group boundaries are tightly policed, the second cases exemplify more permeable boundaries. We conclude by offering tentative hypotheses about the relationship between the permeability of inter-group boundaries and the salience and range of equalization strategies used by members of stigmatized ethno-racial groups to establish equivalence with their counterparts in dominant majority groups

Replication Data for "Assessing the Russian Internet Agency's Impact on the Political Attitudes and Behaviors of U.S. Twitter Users in Late 2017" Proceedings of the National Academy of Sciences

This repository includes data from Bail et al.'s 2019 study "Assessing the Russian Internet Research Agency's Impact on the Political Attitudes and Behaviors of U.S. Twitter Users in Late 2017." The data describe six outcomes (four attitude measures and two behavioral measures) that were collected from surveys fielded for the authors by YouGov in October and November 2017 as well as a measure that describes whether respondents had direct or indirect interaction with accounts associated with the Russian Internet Research Agency by Twitter

Exposure to Opposing Views can Increase Political Polarization

Replication materials for Bail et al. 2018 "Exposure to Opposing Views on Social Media can Increase Political Polarization." Proceedings of the National Academy of Sciences. ***NOTE: Some variables were coarsened using Statistical Disclosure Control Methods in order to protect anonymity of respondents because of risk of identification via social media meta data merged with survey responses*** ABSTRACT There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 mo that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science