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1. Social network analysis for social neuroscientists

   https://doi.org/10.1093/scan/nsaa069  

   Baek, Elisa C; Porter, Mason A; Parkinson, Carolyn (May 2020, Social Cognitive and Affective Neuroscience)

   null (Ed.)

   Abstract Although social neuroscience is concerned with understanding how the brain interacts with its social environment, prevailing research in the field has primarily considered the human brain in isolation, deprived of its rich social context. Emerging work in social neuroscience that leverages tools from network analysis has begun to advance knowledge of how the human brain influences and is influenced by the structures of its social environment. In this paper, we provide an overview of key theory and methods in network analysis (especially for social systems) as an introduction for social neuroscientists who are interested in relating individual cognition to the structures of an individual’s social environments. We also highlight some exciting new work as examples of how to productively use these tools to investigate questions of relevance to social neuroscientists. We include tutorials to help with practical implementations of the concepts that we discuss. We conclude by highlighting a broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems. 

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2. Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence

   Zadeh, Amir; Chan, Michael; Liang, Paul Pu; Tong, Edmund; Morency, Louis-Philippe (January 2019, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR))

   As intelligent systems increasingly blend into our everyday life, artificial social intelligence becomes a prominent area of research. Intelligent systems must be socially intelligent in order to comprehend human intents and maintain a rich level of interaction with humans. Human language offers a unique unconstrained approach to probe through questions and reason through answers about social situations. This unconstrained approach extends previous attempts to model social intelligence through numeric supervision (e.g. sentiment and emotions labels). In this paper, we introduce the Social-IQ, an unconstrained benchmark specifically designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence. The dataset contains rigorously annotated and validated videos, questions and answers, as well as annotations for the complexity level of each question and answer. Social- IQ contains 1, 250 natural in-thewild social situations, 7, 500 questions and 52, 500 correct and incorrect answers. Although humans can reason about social situations with very high accuracy (95.08%), existing state-of-the-art computational models struggle on this task. As a result, Social-IQ brings novel challenges that will spark future research in social intelligence modeling, visual reasoning, and multimodal question answering (QA). 

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3. Human social sensing is an untapped resource for computational social science

   https://doi.org/10.1038/s41586-021-03649-2  

   Galesic, Mirta; Bruine de Bruin, Wändi; Dalege, Jonas; Feld, Scott L.; Kreuter, Frauke; Olsson, Henrik; Prelec, Drazen; Stein, Daniel L.; van der Does, Tamara (July 2021, Nature)

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4. Keyword expansion techniques for mining social movement data on social media

   https://doi.org/10.1140/epjds/s13688-022-00343-9  

   Bozarth, Lia; Budak, Ceren (May 2022, EPJ Data Science)

   Abstract Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random. 

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5. Sequential Deliberation for Social Choice

   Fain, B; Goel, A; Munagala, K; Sakshuwong, S. (November 2017, International Conference on Web and Internet Economics)