Computational Social Science Research Articles

A practitioner-centered policy roadmap for ethical computational social science in Germany, Austria, and Switzerland.

Background: Computational Social Science (CSS) utilizes large digital datasets and computational methods to study human behavior, raising ethical concerns about data privacy, informed consent, and potential misuse.Methods: This study employs a constructivist grounded theory approach, analyzing 15 in-depth interviews with CSS practitioners in Germany, Austria, and Switzerland. These countries share a European legal context regarding data privacy and hereby provide a comparable regulatory environment for examining ethical considerations.Results: Findings highlight key challenges in CSS research, including power imbalances with data providers, uncertainties around surveillance and data privacy (especially with longitudinal data), and limitations of current ethics frameworks. Researchers face tensions between established ethical principles and practical realities, often feeling disempowered and lacking support from ethics boards due to their limited CSS expertise. Regulatory ambiguity further discourages research due to fear of sanctions.Conclusions: To foster responsible CSS practices, this paper recommends establishing specialized ethics boards with CSS expertise. It also advocates for acknowledging CSS's unique nature in research policy by developing tailored data guidelines and providing legal certainty through clear guidelines. Grounding recommendations in practitioners' experiences, this study offers actionable steps to help enable ethical CSS research.

"I'm in the Bluesky Tonight": Insights from a year worth of social data.

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped "like" interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.

Public attitudes towards social media field experiments.

The growing use of social media field experiments demands a rethink of current research ethics in computational social science and psychological research. Here, we provide an exploratory empirical account of key user concerns and outline a number of critical discussions that need to take place to protect participants and help researchers to make use of the novel opportunities of digital data collection and field studies. Our primary contention is that we need to elicit public perceptions to devise more up-to-date guidelines for review boards whilst also allowing and encouraging researchers to arrive at more ethical individual study design choices themselves. To ground our discussion in real-world examples of online experiments, we focus on recent social media studies in the field of misinformation, polarization, and hate speech research. We conclude by discussing how we can better strike a balance between meeting ethical guidelines and the concerns of social media users alongside maximizing scientific impact and credibility.

Political Communication Study: A Review

Political communication is one of the most fundamental and crucial concepts in politics and communication studies. This article systematically reviews existing relevant literature to the definition of political communication and theoretical and empirical study of political communication. It hopes to provide some guidance for this field, with the aim of promoting the development of political communication study. This study finds that digital technology is quietly and powerfully shaping a new political communication landscape and the political communication study is showing an increasingly strong trend of interdisciplinary intersection and integration, with computational and experimental social science methods continuing to empower and become new research directions.

Exploring Twitter discourse with BERTopic: topic modeling of tweets related to the major German parties during the 2021 German federal election

AbstractWe present a study in the context of computational social science that explores the topics debated in the context of the 2021 German Federal Election by using the topic modeling technique BERTopic. The corpus consists of German language tweets posted by political party accounts of the major German parties, as well as tweets by the general public mentioning the party accounts. We examined the textual content of the tweets but also included the text in images that were posted into the analysis by extracting the text using optical character recognition (OCR). Our results show that the most frequently discussed topics are party-oriented policies (including call-to-action content), climate policy and financial policy, with these topics being discussed in tweets by both, the political party accounts and tweets by accounts mentioning them. In addition, we observed that some topics were discussed consistently throughout the year, such as the COVID-19 pandemic, climate policy or digitization, while other topics, such as the return to power of the Taliban in Afghanistan or Israel were debated to a greater extent at limited time frames during the election year.

Deceptively simple: An outsider's perspective on natural language processing

AbstractThis article highlights a collection of ideas with an underlying deceptive simplicity that addresses several practical challenges in computational social science and generative AI safety. These ideas lead to (1) an interpretable and quantifiable framework for political polarization; (2) a language identifier robust to noisy social media text settings; (3) a cross‐lingual semantic sampler that harnesses code‐switching; and (4) a bias audit framework that uncovers shocking racism, antisemitism, misogyny, and other biases in a wide suite of large language models.

Assessing the Challenges in Managing School Funds for Heads of Secondary Schools in Kilolo District, Tanzania

This study sought to identify the “Challenges in Managing School Funds for Heads of Secondary Schools in Kilolo District, Tanzania” through a mixed approach with the population of one District educational officer, six heads of schools, six school bursars and sixty-three secondary school teachers who obtained through purposive sampling and simple random techniques. Questionnaires and interviews used to collect data. Data analysed through thematic analysis and descriptive statistics with the aid of the Statistical Package for Social Sciences (SPSS) computer software. The findings found that heads of secondary schools faced numerous challenges such as inadequate funds from the government, inconsistencies disbursement of money from the government and bureaucracy. In addition, distance (remoteness of the schools), transport cost and time consumer when heads of schools needs procurement budgets approved from the District Educational Officer, poor support from the parents and some staff as well as denying school management training on financial management issues were among the challenges. Lastly, the findings found that training, workshops, provision of seminars will enhancing the heads of schools’ financial management literacy. Moreover, heads of schools should involve other staff members, school management team (SMT), and the use of educational financial guidelines as a source of knowledge and skills to manage schools’ fiscal resources. It also recommended that the heads of schools should adhere to the proper practices of financial management through monetary auditing. Likewise, Tanzania government should provide managerial training, workshops and seminars as the best strategies to diminish financial management challenges as well as introducing financial management courses in teachers’ colleges and universities prepare experts in the schools’ financial management activities.

Plasma glucose, HbA1c, insulin and lipid profile in Sudanese type 2 diabetic patients with cardiovascular disease: a case control study

Background: Type 2 diabetes mellitus (T2DM) and its consequences are a serious global public health issue. By 2030, the number of people with type 2 diabetes is predicted to reach 439 million. The purpose of this study is to evaluate the plasma levels of glucose, HbA1c, insulin, and lipid profile in Sudanese T2DM patients. Methods: This case control study included 165 Sudanese patients with diabetic type 2 and a cardiovascular condition as cases and 165 diabetic type 2 volunteers without a cardiovascular disorder as controls. The concentrations of plasma glucose, HbA1c, and lipid profile were assessed using a Mindray BS-480 auto-chemistry analyzer, and insulin was analyzed using a Cobase 411 auto analyzer. The collected data were analyzed using statistical tools for social science computer programs (SPSS version 21). Results: According to the findings, (59.4%) of patients between the ages of (50-69). Females made up 50.9%. (38.2%) of patients had an illness duration of between (8-15 years). (41.8%) of individuals did not have hypertension. There was a substantial rise in BMI, FBG, HbA1c, HDL-C, and insulin among diabetics with cardiovascular disease compared to diabetics without cardiovascular disease (p-value = 0.001, 0.000, 0.018, and 0.000). Females had significantly higher blood TC, LDL-C, HDL-C, and BMI than males (p-values = 0.000, 0,001, and 0.000, respectively). There were significant positive correlation between FBS, HBA1c, insulin and duration of disease (r=0.155, p, value=0.005) (r=0.160, p, value=0.004)(r=0.103, p. value=0.061)respectively, while there were significant negative correlation between TC, TG,LDL-C, HDL-C and duration of disease (r=-0.152, p, value= 0.006)(r=-0.023, p, value=0.678)(r=-0.113, p, value= 0.040)(r=-0.145, p, value=0.008)respectively. Conclusion: When comparing diabetics with cardiovascular disease to diabetics without cardiovascular disease, there was a substantial rise in BMI, FBG, HbA1c, HDL-C, and insulin. FBS, HBA1c, insulin, and illness duration all had a strong positive connection.

Coconut Libtool: Bridging Textual Analysis Gaps for Non‐Programmers

ABSTRACTIn the era of big and ubiquitous data, professionals and students alike are finding themselves needing to perform a number of textual analysis tasks. Historically, the general lack of statistical expertise and programming skills has stopped many with humanities or social sciences backgrounds from performing and fully benefiting from such analyses. Thus, we introduce Coconut Libtool (www.coconut-libtool.com/), an open‐source, web‐based application that utilizes state‐of‐the‐art natural language processing (NLP) technologies. Coconut Libtool analyzes text data from customized files and bibliographic databases such as Web of Science, Scopus, and Lens. Users can verify which functions can be performed with the data they have. Coconut Libtool deploys multiple algorithmic NLP techniques at the backend, including topic modeling (LDA, Biterm, and BERTopic algorithms), network graph visualization, keyword lemmatization, and sunburst visualization. Coconut Libtool is the people‐first web application designed to be used by professionals, researchers, and students in the information sciences, digital humanities, and computational social sciences domains to promote transparency, reproducibility, accessibility, reciprocity, and responsibility in research practices.

User-Centric Behavioral Tracking: Lessons from Three Case Studies with Do-It-Yourself Computational Pipelines

User-centric behavioral tracking, a cutting-edge computational social science tool, holds tremendous promise for advertising research. The article introduces the technique and presents three do-it-yourself (DIY) case studies in which researchers develop tracking applications and platforms, build infrastructures that host participants, maintain computational pipelines logging user behavior and content exposure, and manage logistical “ins and outs” such as onboarding, offboarding, and compensations all by themselves. We share our lessons, discuss challenges ahead for DIY user-centric behavioral tracking, and advocate for computational advertising scholars to become pioneers in this emerging body of work.

Understanding Researchers’ Perspectives on Work Tasks in Digital Humanities and Computational Social Sciences

The usefulness of research infrastructures (RIs) for digital humanities (DH) and computational social sciences (CSS) depends on their capability to support research work tasks. RIs can provide more effective support and services when they have a clear understanding of the work practices and specific tasks researchers are engaged in. This paper explores the ways of working of social sciences and humanities (SSH) scholars to develop resources that truly endorse their data-intensive research processes. Particularly, the study investigates how SSH scholars interact with digital tools and materials to both determine their information needs and how to better support them. A qualitative analysis of 21 semi-structured interviews with potential end-users of a national RI for the DH and CSS that is currently under development revealed three themes: digitizing SSH research, meeting the information needs related to DH/CSS, and supporting DH/CSS research. Based on our findings, we were able to create an understanding of the work task-based requirements of SSH scholars that can be used to inform designs for improved RIs. Suggestions for enhancing the sustainability of resources and services in SSH are also put forth, emphasizing the importance of understanding and accommodating the unique scholarly practices within these fields.

Automatic gender detection: a methodological procedure and recommendations to computationally infer the gender from names with ChatGPT and gender APIs

AbstractBoth computational social scientists and scientometric scholars alike, interested in gender-related research questions, need to classify the gender of observations. However, in most public and private databases, this information is typically unavailable, making it difficult to design studies aimed at understanding the role of gender in influencing citizens’ perceptions, attitudes, and behaviors. Against this backdrop, it is essential to design methodological procedures to infer the gender automatically and computationally from data already provided, thus facilitating the exploration and examination of gender-related research questions or hypotheses. Researchers can use automatic gender detection tools like Namsor or Gender-API, which are already on the market. However, recent developments in conversational bots offer a new, still relatively underexplored, alternative. This study offers a step-by-step research guide, with relevant examples and detailed clarifications, to automatically classify the gender from names through ChatGPT and two partially free gender detection tool (Namsor and Gender-API). In addition, the study provides methodological suggestions and recommendations on how to gather, interpret, and report results coming from both platforms. The study methodologically contributes to the scientometric literature by describing an easy-to-execute methodological procedure that enables the computational codification of gender from names. This procedure could be implemented by scholars without advanced computing skills.

Bootstrapping public entities. Domain-specific NER for public speakers

ABSTRACT Named Entity Recognition (NER) is a supervised machine learning task that finds various applications in automated content analysis, as the identification of entities is vital for understanding public discourse. However, sometimes the standard NER labels are not specific enough for a given domain. We introduce Public Entity Recognition (PER). PER is a domain-specific version of NER, that is trained for five entity types that are common to public discourse: politicians, parties, authorities, media, and journalists. PER can be used for pre-processing documents, in a pipeline with other classifiers or directly for analyzing information in texts. The taxonomy for PER is taken from the database of (German) public speakers and aims at low-threshold integration into computational social science research. We experiment with different training settings, involving weakly supervised training and training on manually annotated data. We evaluate multilingual transformer models of different sizes against rule-based entity matching and find that the models do not only outperform the baseline but also reach competitive absolute scores of around .8 and higher in F1. We further test for generalization and domain adaptation. We show that with only around 100–150 additional sentences, the model can be adapted to new languages.

Utilising interaction features on Twitter/now X to understand the 'messaging' of migration amongst non-elite users

Different aspects of interactions on social media — communication and action — imply distinctive ways of knowing the social world. I present a new methodological approach that utilizes ‘big’ social media data to understand politically salient issues such as the ‘messaging’ of migration on Twitter/now X. An iterative abductive interpretivist analytical strategy drawing on computational and qualitative social science techniques was applied to a corpus of 47,978 tweets created over five months around the time of lifting of temporary controls on free movement from Romania and Bulgaria to the U.K. in January 2014. Initial computational network analysis on the retweet action feature revealed a small number of highly influential users and a large proportion of isolated users (non-elites) who were never retweeted. Given paucity of understanding of how elite narratives on migration are absorbed, accepted or contested by non-elites, the next stage involved qualitative thematic analysis of a sub-sample of actual tweets (communication) from non-elites to understand meaning-making in views expressed. Qualitative analysis confirmed presence of highly polarised immigration attitudes amongst non-elites but also revealed their values and beliefs about national belonging. These findings prompted questions about what or who influences these values amongst non-elites and whether there are any structural differences in information flows amongst anti- and pro-immigration users. In the third stage, computational surface thematic mapping of different aspects of communication and action in the whole corpus revealed importance of the entire media environment but also differences in the presence or lack of echo-chambers amongst those expressing anti- or pro-immigrant sentiments. This article demonstrates the potential of cross-disciplinary analytical strategies when investigating politically salient issues on social media.

Digital battleground: An examination of anti-refugee discourse on Twitter against Ukrainians displaced by Russia’s invasion of Ukraine

Russia’s war of aggression in Ukraine has triggered Europe’s largest refugee crisis since World War II. In this case study, we investigate the prevalence and types of anti-refugee discourse about Ukrainian refugees on Twitter. Previous studies primarily focused on public discourse and attitudes toward racialized refugees and immigrants; the Ukrainian refugee crisis is unique in that it is one of the few instances of a recent refugee crisis involving people who do not come from mostly racialized communities. Using Communalytic, a computational social science tool for studying public discourse on social media, we automatically collected and identified toxic posts mentioning Ukrainian refugees during the first year of Russia’s full-scale invasion of Ukraine. We focused on posts containing toxic language, as this is where we are most likely to find examples of anti-refugee sentiments. Based on a manual analysis of 2,045 toxic posts referencing Ukrainian refugees, the most prevalent ones were politically motivated and included partisan content (33 percent), followed by posts containing expressions countering anti-refugee narratives (20 percent). These findings highlight the escalating politicization and polarization of discussions about Ukrainian refugees both online and offline. Furthermore, 53 percent of the sample aligned with pro-Kremlin narratives against Ukraine. By exploiting anti-refugee sentiments and leveraging existing political and cultural fault lines in the West, pro-Kremlin messages on Twitter contribute to diminishing support for Ukrainian refugees, minimizing the severity of the war, and undermining international support for Ukraine.

A framework for extending co-creative communication models to sustainability research.

The UN Sustainable Development Goals (SDGs) present a challenge due to their potential for conflicting objectives, which hinders their effective implementation. In order to address the complexity of sustainability issues, a framework capable of capturing the specificity of diverse sustainability issues while offering a common methodology applicable across contexts is required. Co-creative communication can be regarded as a key source of uncertainty within functional systems, as it can be instrumental in realizing and sustaining sustainability. In this regard, the studies in Constructive approaches to Co-creative Communication (CCC), particularly those employing artificial intelligence (AI) methodologies such as computational social science and innovation studies, hold significant value for both theoretical and applied sustainability research. However, existing CCC frameworks cannot be directly applied to sustainability research. This work bridges this gap by proposing a framework that outlines a general approach to establishing formalized definitions of sustainability from the lens of communication. This approach enables the direct application of CCC models to sustainability studies. The framework is based on systems theory and the methodologies of artificial intelligence, including computational/symbolic modeling and formal methods. This framework emphasizes the social function of co-creative communication and the interaction between the innovation process and the sustainability of the system. It can be concluded that the application of our framework enables the achievements of CCC to be directly applied to sustainability research. Researchers from different disciplines are therefore able to establish their own specific definitions of sustainability, which are tailored to their particular concerns. Our framework lays the groundwork for future sustainability studies that employs CCC, facilitating the integration of CCC insights into sustainability research and application. The outcomes of computational creativity research based on AI technologies, such as distributed artificial intelligence and self-organizing networks, can deepen the understanding of sustainability mechanisms and drive their practical applications. Furthermore, the functional role of co-creative communication in societal sustainability proposed in this work offers a novel perspective for future discussions on the evolutionary adaptation of co-creative communication.

On the need for integrating social sciences and engineering – technical disciplines in studying contemporary social phenomena. New directions in digital sociology/O potrzebie integracji nauk społecznych i dyscyplin inżynieryjno-technicznych w badaniu współczesnych zjawisk społecznych. Nowe kierunki w socjologii cyfrowej

The main aim of this article is to emphasize the significance of adopting an interdisciplinary approach when researching contemporary social phenomena. It identifies the shortcomings of traditional sociology, which frequently lacks the digital tools to effectively analyse these phenomena, and contrasts them with engineering approaches that frequently employ computer science methods. This article advocates for a combination of these disciplines, which not only promotes the development of innovative methods but also contributes to the emergence of sub-disciplines such as Digital Sociology, Computational Social Science, and Digital Social Science. The argument presented is grounded in Actor-Network Theory (ANT), which provides a robust framework for understanding the dynamic interplay between society and technology, acknowledging both human and non-human entities as active participants in social networks. This approach challenges traditional perspectives and encourages a comprehensive reassessment of social interactions and technological impacts.

The Reddit cannabis subjective highness rating scale: Applying computational social science to explore psychological and environmental correlates of naturalistic cannabis use.

Social media data provide unprecedented access to discussions of active, naturalistic, and often real-time cannabis use in an era of cannabis policy liberalization. The aim of this study was to explore psychological and environmental correlates of cannabis effects by applying computational social science approaches to a large dataset of unprompted reports of naturalistic cannabis use with corresponding self-reported numerical ratings of subjective highness. Post title text was extracted via the Pushshift dataset from N = 328,865 posts to the r/trees Reddit community, where posters self-assess and disclose how high they feel on a scale from 1 to 10 (M = 6.9, SD = 1.8). Structural topic modelling and Linguistic Inquiry and Word Count (LIWC) dictionary-based approaches were applied to identify (1) frequently discussed topics and (2) text indicative of 5 psychological processes (affective, social, cognitive, perceptual, biological), respectively, as well as to examine relationships between subjective highness and (1) topic prevalence and (2) psychological process word counts. A 40-topic model was selected for interpretation based on semantic coherence and exclusivity. The most discussed topics in a 40-topic model were characterized by references to smoking places, social contexts, positive affect, cognitive states, as well as food and media consumed. In LIWC dictionary analyses, words mentioning affective, social, and cognitive processes were referenced more often than perceptual or body processes. Posters reported greater subjective highness when using language that referred to in-person social environments and lower subjective highness when using language that referred to online social environments and positive affect psychological states. This examination of unprompted online reports of naturalistic cannabis use identified textual content referring to affect and to other people as being associated with perceived effects of cannabis. These affective and social aspects of the cannabis use experience were salient to active posters in this online community and should be integrated into experience sampling methods and behavioral pharmacology research, as well as public health messaging.

Quantifying polarization in online political discourse

In an era of increasing political polarization, its analysis becomes crucial for the understanding of democratic dynamics. This paper presents a comprehensive research on measuring political polarization on X (Twitter) during election cycles in Spain, from 2011 to 2019. A wide comparative analysis is performed on algorithms used to identify and measure polarization or controversy on microblogging platforms. This analysis is specifically tailored towards publications made by official political party accounts during pre-campaign, campaign, election day, and the week post-election. Guided by the findings of this comparative evaluation, we propose a novel algorithm better suited to capture polarization in the context of political events, which is validated with real data. As a consequence, our research contributes a significant advancement in the field of political science, social network analysis, and overall computational social science, by providing a realistic method to capture polarization from online political discourse.

Bridging The Gap Between Tourism Science And Vocational Education: Strategies To Increase Industry Relevance In Indonesia

Indonesia's tourism industry is growing rapidly, but the gap between tourism science and vocational education continues to hinder the development of quality human resources. This journal examines strategies to bridge this gap and increase industry relevance, focusing on the Indonesian context. A comprehensive literature review identified several key strategies, including: Computational Social Science Integration, Industry-University Collaboration, Executive-in-Residence Models, and Industry Engagement in Curriculum Development. The effective implementation of these strategies can increase the relevance of tourism vocational education to the industry, producing a skilled and work-ready workforce that contributes to the growth and sustainability of Indonesia's tourism industry.