Weicheng Ma, Hefan Zhang, Ivory Yang, Shiyu Ji, Joice Chen, Farnoosh Hashemi, Shubham Mohole, Ethan Gearey, Michael Macy, Saeed Hassanpour, Soroush Vosoughi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

We describe and validate a system for monitoring social contagions on Twitter: social movements, rumors, and emotional outbursts that spread from person to person in a viral manner. We use Twitter streams to monitor the spread of these phenomena through human social and information networks. This system, the contagion monitor, parses Twitter posts to identify emerging phenomena, as captured in hashtags, URLs, words and phrases, or account-handles, and then determines the extent to which a particular phenomenon spreads via the social network (in contrast to its spread via news broadcasts or independent adoption) and locates the contagion within Twitter communities. The monitor approximates the adoption threshold of a social contagion by measuring the fraction of Twitter users who were “infected” by the contagion (e.g., joined a particular social movement) after more than one of their friends had done so. Finally, the monitor makes a judgment about whether the phenomenon has reached critical mass, which is defined as the point where a social contagion begins spreading rapidly and breaches the social boundaries of its early adopter group. We test our prototype monitor on two data sources - an ongoing stream of tweets grouped by user-added hashtags and a collection of posts by a monitored set of Nigerian Twitter users - before productionalizing. We use the Amazon Mechanical Turk platform to evaluate the performance on both data sources. In both cases, we find that our approach successfully distinguishes between high-threshold and low-threshold social contagions.

In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.

Abduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.

We investigate the use of spectral clustering of hashtag adoptions in Nigerian Twitter users between October 2013 and November 2014. This period is of interest due to the online campaign centered around the #BringBackOurGirls (BBOG) hashtag, which relates to the kidnapping of 276 Nigerian schoolgirls. We examine the adoption of hashtags during the six months before, the month after, and the six months following the kidnapping to test the informational value of behavior-based clusters discovered with unsupervised methods for predicting future hashtag usage behaviors. We demonstrate an efficient spectral clustering approach, that leverages power iteration on symmetric adjacency matrices, to group users based on hashtag adoptions prior to the kidnapping. Unlike follow networkbased clusters, these adoption-based clusters reveal groups of users with similar interests and prove to be more predictive of interest in future topics. We compare this unsupervised spectral clustering to spectral clustering based on symmetrized follow network relations as well as clusters induced by latent Dirichlet allocation (LDA) topics. We find that hashtag adoptionbased clusters perform similarly to the more computationally expensive LDA approach at identifying interest groups that are more likely to adopt future topical tags. We also benchmark the spectral clustering approach against the popular Louvain clustering approach on a synthetic dataset, finding the faster spectral clustering algorithm produces more balanced clusters with a higher similarity to the true interest groupings used to synthesize adoption data.

The use of bots as virtual confederates in online field experiments holds extreme promise as a new methodological tool in computational social science. However, this potential tool comes with inherent ethical challenges. Informed consent can be difficult to obtain in many cases, and the use of confederates necessarily implies the use of deception. In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation. We draw upon examples from prior work in the CSCW community and the broader social science literature for illustration. While a handful of prior researchers have used bots in online experimentation, our work is meant to inspire future work in this area and raise awareness of the associated ethical issues.

We have implemented a social media data mining system capable of forecasting events related to Latin American social unrest. Our method directly extracts a small number of tweets from publicly-available data on twitter.com, condenses similar tweets into coherent forecasts, and assembles a detailed and easily-interpretable audit trail which allows end users to quickly collect information about an upcoming event. Our system functions by continually applying multiple textual and geographic filters to a large volume of data streaming from twitter.com via the public API as well as a commercial data feed. To be specific, we search the entirety of twitter.com for a few carefully chosen keywords, search within those tweets for mentions of future dates, filter again using various logistic regression classifiers, and finally assign a location to an event by geocoding retweeters. Geocoding is done using our previously-developed in-house geocoding service which, at the time of this writing, can infer the home location for over 62M twitter.com users [1]. Additionally, we identify demographics likely interested in an upcoming event by searching retweeter's recent posts for demographic-specific keywords.