From Symbols to Embeddings:A Tale of Two Representations in Computational Social Science

Computational Social Science(CSS),aiming at utilizing computational methods to address social science problems,is a recent emerging and fast-developing field.The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks,which contain rich text and network data for investigation.However,these large-scale and multi-modal data also present researchers with a great challenge:how to represent data effectively to mine the meanings we want in CSS?To explore the answer,we give a thorough review of data representations in CSS for both text and network.Specifically,we summarize existing representations into two schemes,namely symbol-based and embeddingbased representations,and introduce a series of typical methods for each scheme.Afterwards,we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS.From the statistics of these applications,we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade.Finally,we discuss several key challenges and open issues for future directions.This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.

Social Computing Unhinged

Social computing is ubiquitous and intensifying in the 21st Century.Originally used to reference computational augmentation of social interaction through collaborative filtering,social media,wikis,and crowdsourcing,here I propose to expand the concept to cover the complete dynamic interface between social interaction and computation,including computationally enhanced sociality and social science,socially enhanced computing and computer science,and their increasingly complex combination for mutual enhancement.This recommends that we reimagine Computational Social Science as Social Computing,not merely using computational tools to make sense of the contemporary explosion of social data,but also recognizing societies as emergent computers of more or less collective intelligence,innovation and flourishing.It further proposes we imagine a socially inspired computer science that takes these insights into account as we build machines not merely to substitute for human cognition,but radically complement it.This leads to a vision of social computing as an extreme form of human computer interaction,whereby machines and persons recursively combine to augment one another in generating collective intelligence,enhanced knowledge,and other social goods unattainable without each other.Using the example of science and technology,I illustrate how progress in each of these areas unleash advances in the others and the beneficial relationship between the technology and science of social computing,which reveals limits of sociality and computation,and stimulates our imagination about how they can reach past those limits together.

Negative Sentiment Shift on a Chinese Movie-Rating Website

Shifting to negativity is more and more prevalent in online communities and may play a key role in group polarization.While current research indicates a close relationship between group polarization and negative sentiment,they often link negative sentiment shifts with echo chambers and misinformation within echo chambers.In this work,we explore the sentiment drift using over 4 million comments from a Chinese online movie-rating community that is less affected by misinformation than other mainstream online communities and has no echo chamber structures.We measure the sentiment shift of the community and users of different engagement levels.Our analysis reveals that while the community does not show a tendency toward negativity,users of higher engagement levels are generally more negative,considering factors like the different movies they consume.The results indicate a fitting-in process,suggesting the possible mechanism of group identity on sentiment shift on social media platforms.These findings also provide guidance on web design to tackle the negativity issue and expand sentiment shift analysis to non-English contexts.

Argumentative Conversational Agents for Online Discussions

Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.

Pandemic Policymaking

This study leverages a high dimensional manifold learning design to explore the latent structure of the pandemic policymaking space only based on bill-level characteristics of pandemic-focused bills from 1973 to 2020.Results indicate the COVID-19 era of policymaking maps extremely closely onto prior periods of related policymaking.This suggests that there is striking uniformity in Congressional policymaking related to these types of large-scale crises over time,despite currently operating in a unique era of hyperpolarization,division,and ineffective governance.

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

The COVID-19 pandemic has severely harmed every aspect of our daily lives,resulting in a slew of social problems.Therefore,it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery.To this end,various types of social sensing tools,such as tweeting and publicly released news,have been employed to understand individuals’and communities’thoughts,behaviors,and attitudes during the COVID-19 pandemic.However,some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19.This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience.We use fact-checking organizations to classify news as real,mixed,or fake,and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience(CR).Based on the news articles and tweets collected,we quantify CR based on two key factors,community wellbeing and resource distribution,where resource distribution is assessed by the level of economic resilience and community capital.Based on the estimates of these two factors,we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets.To improve the operationalization and sociological significance of this work,we use dimension reduction techniques to integrate the dimensions.