Am 20.11.2018 wurde im Lichthof der Leibniz Universität Hannover Forschung und Startups rund um das Thema künstliche Intelligenz vorgestellt.  Zusammen mit dem Forschungszentrum L3S von der Universität Hannover und 1KLANG®, die Marke der PROJEKTIONISTEN® GmbH, das Forschungsprojekt Data4UrbanMobility vorstellen.
Interessierte wurden über die Ergebnisse aus dem Projekt in Bezug auf die entwickelten Apps und das Dashboard informiert (s. unter Ergebnisse: https://data4urbanmobility.l3s.uni-hannover.de/index.php/de/ergebnisse/).

Wir freuen uns über die spannenden Gespräche, die geführt werden konnten und die neuen Tester, die uns bei der Citizen Science App „MiC – Move in the City“ unterstützen möchten.

• Comparing Methods for Creating a National Random Sample of Twitter Users. Alizadeh, Meysam; Zare, Darya; Samei, Zeynab; Alizadeh, Mohammadamin; Kubli, Maël; Aliahmadi, Mohammadhadi; Ebrahimi, Sarvenaz; Gilardi, Fabrizio (2024). abs/2402.04879
• Affective Polarization and Dynamics of Information Spread in Online Networks. Lerman, Kristina; Feldman, Dan; He, Zihao; Rao, Ashwin (2024).
• Cutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments. Möller, A. Marthe; Vermeer, Susan A. M.; Baumgartner, Susanne E. (2024). 42(1) 162–185.

  Social scientists often study comments on YouTube to learn about people’s attitudes towards and experiences of online videos. However, not all YouTube comments are relevant in the sense that they reflect individuals’ thoughts about, or experiences of the content of a video or its artist/maker. Therefore, the present paper employs Supervised Machine Learning to automatically assess comments written in response to music videos in terms of their relevance. For those comments that are relevant, we also assess why they are relevant. Our results indicate that most YouTube comments are relevant (approx. 78\%). Among those, most are relevant because they include a positive evaluation of the video, describe a viewer’s personal experience related to the video, or express a sense of community among the video viewers. We conclude that Supervised Machine Learning is a suitable method to find those YouTube comments that are relevant to scholars studying viewers’ reactions to online videos, and we present suggestions for scholars wanting to apply the same technique in their own projects.
• Performing an Inductive Thematic Analysis of Semi-Structured Interviews With a Large Language Model: An Exploration and Provocation on the Limits of the Approach. Paoli, Stefano De (2024). 42(4) 997–1019.

  Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions. This paper presents results and reflections of an experiment done with the LLM GPT 3.5-Turbo to perform an inductive Thematic Analysis (TA). Previous research has worked on conducting deductive analysis. Thematic Analysis is a qualitative method for analysis commonly used in social sciences and it is based on interpretations by the human analyst(s) and the identification of explicit and latent meanings in qualitative data. The paper presents the motivations for attempting this analysis; it reflects on how the six phases to a TA proposed by Braun and Clarke can partially be reproduced with the LLM and it reflects on what are the model’s outputs. The paper uses two datasets of open access semi-structured interviews, previously analysed by other researchers. The first dataset contains interviews with videogame players, and the second is a dataset of interviews with lecturers teaching data science in a University. This paper used the analyses previously conducted on these datasets to compare with the results produced by the LLM. The results show that the model can infer most of the main themes from previous research. This shows that using LLMs to perform an inductive TA is viable and offers a good degree of validity. The discussion offers some recommendations for working with LLMs in qualitative analysis.
• An open window into politics: A structured database of plenary sessions of the European Parliament. Tremblay-Antoine, Camille; Jacob, Steve; Dufresne, Yannick; Poncet, Patrick; Dinan, Shannon (2024). 25(3) 605–622.

  The uniqueness of the European Parliament, as well as the magnitude of impact its decisions wield over member states, are elements that capture researchers’ attention. However, several of this institution’s particularities have made broad analysis of the textual content it produces difficult. This research note presents Vitrine Démocratique, a new, publicly accessible, and centralized database structuring interventions made in the European Parliament starting in 2014, both in their original languages and translated to English. The process by which this high-velocity database was created is presented, as well as a descriptive overview of the contents of this data source, which is continuously updated on a daily basis.
• How can online citizen complaints provide solutions to refine environmental management: A spatio-temporal perspective. Jiao, Yaran; Li, Chunming; Yao, Ziyan; Weng, Chen; Lian, Anxin; Dong, Rencai (2024). 61(2) 103611.

  E-government serves as a crucial platform for citizens to voice their demands, providing valuable data for understanding public concerns. However, limited research has explored the textual features of citizen complaints and their implications for environmental management. This study integrates time analysis, spatial hotspot analysis, and influencing factor analysis, building upon sentiment analysis of 7,657 environmental complaints (from 2018 to 2021) in Guangzhou City, China, to elucidate the specific contribution of citizen complaints in improving environmental management solutions. The findings reveal that environmental complaints exhibit time-sensitive and spatially diverse patterns, and the influence of socioeconomic factors on environmental complaints varies in terms of direction and intensity. The study reveals that the majority of environmental complaints (94.14 %) exhibit negative emotions. The sentiment of complaints is found to be time-sensitive, with significant variations in issues related to air, noise, and light. Interestingly, the hot spots of environmental complaints do not necessarily align with areas of high negative sentiment, highlighting the need for targeted interventions in key regions. The analysis identifies the influence of various factors, such as entertainment venues, industrial activities, and land use patterns, on different types of complaints. Notably, the study reveals no statistical correlation between complaint response efficiency and sentiment, emphasizing the importance of considering emotional intensity in government responses. These findings contribute to the advancement of refined environmental engagement and management.
• Emotion-cognitive reasoning integrated BERT for sentiment analysis of online public opinions on emergencies. Wan, Bingtao; Wu, Peng; Yeo, Chai Kiat; Li, Gang (2024). 61(2) 103609.

  Sentiment analysis of online public opinions on emergencies (OPOEs) requires accurate and explainable results to facilitate a better understanding of public sentiment and effective crisis management, but it is challenging due to the complexity and diversity of emotions contained in OPOEs. In this paper, we propose an Emotion-Cognitive Reasoning integrated BERT (ECR-BERT) for sentiment analysis of OPOEs. ECR-BERT combines an emotion model and deep learning to provide reliable auxiliary knowledge to improve BERT. Specifically, we use the emotion model proposed by Ortony, Clore, and Collins (OCC) to build emotion-cognitive rules and perform emotion-cognitive reasoning to discover emotion-cognitive knowledge. To mitigate the impact of knowledge noise, we propose a novel self-adaptive fusion algorithm that provides a selection mechanism for the incorporation of knowledge. In addition, we utilize knowledge-enabled feature representation to efficiently exploit inferred knowledge. Our evaluation on four real-world OPOE datasets shows that ECR-BERT significantly outperforms other BERT-based models, achieving state-of-the-art results with an absolute average accuracy improvement of 0.82%, 1.74%, 0.98%, and 1.37% over BERT, respectively. In addition, ECR-BERT provides a detailed explanation of how sentiment polarity is derived from fine-grained emotion categories. The ablation study demonstrates the effectiveness of each technique. In conclusion, ECR-BERT is an excellent choice for sentiment analysis of OPOEs, providing accurate and explainable results for crisis management.
• Topic Shifts as a Proxy for Assessing Politicization in Social Media. Locatelli, Marcelo Sartori; Calais, Pedro; Miranda, Matheus Prado; Junho, João Pedro; Muniz, Tomas Lacerda; au2, Wagner Meira Jr.; Almeida, Virgilio (2024).
• 1-year weight change after diabetes diagnosis and long-term incidence and sustainability of remission of type 2 diabetes in real-world settings in Hong Kong: An observational cohort study. Wu, Hongjiang; Yang, Aimin; Lau, Eric S. H.; Zhang, Xinge; Fan, Baoqi; Ma, Ronald C. W.; Kong, Alice P. S.; Chow, Elaine; So, Wing-Yee; Chan, Juliana C. N.; Luk, Andrea O. Y. (2024). 21(1) 1–19.

  Background Clinical trials have demonstrated that remission of type 2 diabetes can be achieved following sustained weight loss. However, the feasibility of achieving diabetes remission through weight management in real-world settings remains unclear. In this study, we aimed to examine the association of weight change at 1 year after diabetes diagnosis with long-term incidence and sustainability of type 2 diabetes remission in real-world settings in Hong Kong. Methods and findings This was a population-based observational cohort study. The territory-wide Risk Assessment and Management Programme for Diabetes Mellitus (RAMP-DM) provides regular comprehensive assessments of metabolic control and complication screening for people with diabetes in Hong Kong. We included 37,326 people with newly diagnosed type 2 diabetes who were enrolled in the RAMP-DM between 2000 and 2017, followed until 2019. Diabetes remission was defined as 2 consecutive HbA1c <6.5% measurements at least 6 months apart in the absence of glucose-lowering drugs (GLDs) and with no record of GLDs at least 3 months before these measurements. During a median follow-up of 7.9 years, 6.1% (2,279) of people achieved diabetes remission, with an incidence rate of 7.8 (95% CI: 7.5, 8.1) per 1,000 person-years. After adjusting for age at diabetes diagnosis, sex, assessment year, body mass index, other metabolic indices, smoking, alcohol drinking, and medication use, the hazard ratio (HR) for diabetes remission was 3.28 (95% CI: 2.75, 3.92; p < 0.001) for people with ≥10% weight loss within 1 year of diagnosis, 2.29 (95% CI: 2.03, 2.59; p < 0.001) for those with 5% to 9.9% weight loss, and 1.34 (95% CI: 1.22, 1.47; p < 0.001) for those with 0% to 4.9% weight loss compared to people with weight gain. During a median follow-up of 3.1 years, 67.2% (1,531) of people who had achieved diabetes remission returned to hyperglycaemia, with an incidence rate of 184.8 (95% CI: 175.5, 194.0) per 1,000 person-years. The adjusted HR for returning to hyperglycaemia was 0.52 (95% CI: 0.41, 0.65; p < 0.001) for people with ≥10% weight loss, 0.78 (95% CI: 0.68, 0.92; p = 0.002) for those with 5% to 9.9% weight loss, and 0.90 (95% CI: 0.80, 1.01; p = 0.073) for those with 0% to 4.9% weight loss compared to people with weight gain. Diabetes remission was associated with a 31% (HR: 0.69, 95% CI: 0.52, 0.93; p = 0.014) decreased risk of all-cause mortality. The main limitation of the study is that the reliability of HbA1c used to define diabetes remission can be affected by other medical conditions. Furthermore, we did not have data on bariatric surgery. Conclusions In this study, greater weight loss within the first year of diabetes diagnosis was associated with an increased likelihood of achieving diabetes remission and a decreased risk of returning to hyperglycaemia among those who had achieved diabetes remission. However, both the incidence of diabetes remission and the probability of its long-term sustainability were low with conventional management in real-world settings, in an era when the importance of weight loss was not fully appreciated. Our study provides evidence for policymakers to design and implement early weight management interventions and diabetes remission initiatives.
• The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias. Spinde, Timo; Hinterreiter, Smi; Haak, Fabian; Ruas, Terry; Giese, Helge; Meuschke, Norman; Gipp, Bela (2024).
• Talking Past Each Other on Twitter: Thematic, Event, and Temporal Divergences in Polarized Partisan Expression on Immigration. Xiaoya, Jiang (2024). 41(2) 244–268.
• Stance Detection with Explanations. Saha, Rudra Ranajee; Lakshmanan, Laks V. S.; Ng, Raymond T. (2024). 50(1) 193–235.
• Understanding political divisiveness using online participation data from the 2022 French and Brazilian presidential elections. Navarrete, Carlos; Macedo, Mariana; Colley, Rachael; Zhang, Jingling; Ferrada, Nicole; Mello, Maria Eduarda; Lira, Rodrigo; Bastos-Filho, Carmelo; Grandi, Umberto; Lang, Jérôme; Hidalgo, César A. (2024). 8(1) 137–148.

  Digital technologies can augment civic participation by facilitating the expression of detailed political preferences. Yet, digital participation efforts often rely on methods optimized for elections involving a few candidates. Here we present data collected in an online experiment where participants built personalized government programmes by combining policies proposed by the candidates of the 2022 French and Brazilian presidential elections. We use this data to explore aggregates complementing those used in social choice theory, finding that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals. These metrics provide a score for the divisiveness of each proposal that can be estimated in the absence of data on the demographic characteristics of participants and that explains the issues that divide a population. These findings suggest that divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.
• Information practices in data analytics for supporting public health surveillance. Zhang, Dan; Pee, Loo G.; Pan, Shan L.; Wang, Jingyuan (2024). 75(1) 79–93.

  Abstract Public health surveillance based on data analytics plays a crucial role in detecting and responding to public health crises, such as infectious disease outbreaks. Previous information science research on the topic has focused on developing analytical algorithms and visualization tools. This study seeks to extend the research by investigating information practices in data analytics for public health surveillance. Through a case study of how data analytics was conducted for surveilling Influenza A and COVID-19 outbreaks, both exploration information practices (i.e., probing, synthesizing, exchanging) and exploitation information practices (i.e., scavenging, adapting, outreaching) were identified and detailed. These findings enrich our empirical understanding of how data analytics can be implemented to support public health surveillance.
• Detecting Topics and Polarity From Twitter: A University Faculty Case. Ruíz, Almudena Sánchez; Galan, D; García-Beltrán, Ángel; Rodríguez-Vidal, Javier (2024). 12 148–156.
• The social anatomy of climate change denial in the United States. Gounaridis, Dimitrios; Newell, Joshua P. (2024). 14(1) 2097.

  Using data from Twitter (now X), this study deploys artificial intelligence (AI) and network analysis to map and profile climate change denialism across the United States. We estimate that 14.8% of Americans do not believe in climate change. This denialism is highest in the central and southern U.S. However, it also persists in clusters within states (e.g., California) where belief in climate change is high. Political affiliation has the strongest correlation, followed by level of education, COVID-19 vaccination rates, carbon intensity of the regional economy, and income. The analysis reveals how a coordinated social media network uses periodic events, such as cold weather and climate conferences, to sow disbelief about climate change and science, in general. Donald Trump was the strongest influencer in this network, followed by conservative media outlets and right-wing activists. As a form of knowledge vulnerability, climate denialism renders communities unprepared to take steps to increase resilience. As with other forms of misinformation, social media companies (e.g., X, Facebook, YouTube, TikTok) should flag accounts that spread falsehoods about climate change and collaborate on targeted educational campaigns.
• A Dataset for the Detection of Dehumanizing Language. Engelmann, Paul; Trolle, Peter Brunsgaard; Hardmeier, Christian (2024).
• Close to Human-Level Agreement: Tracing Journeys of Violent Speech in Incel Posts with GPT-4-Enhanced Annotations. Matter, Daniel; Schirmer, Miriam; Grinberg, Nir; Pfeffer, Jürgen (2024).
• A Latent Dirichlet Allocation (LDA) Semantic Text Analytics Approach to Explore Topical Features in Charity Crowdfunding Campaigns. Muzumdar, Prathamesh; Kurian, George; Basyal, Ganga Prasad (2024).
• Account credibility inference based on news-sharing networks. Truong, Bao Tran; Allen, Oliver Melbourne; Menczer, Filippo (2024). 13(1) 10.

  The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information diffusion patterns, in particular leveraging two networks: the reshare network, capturing an account's trust in other accounts, and the bipartite account-source network, capturing an account's trust in media sources. We extend network centrality measures and graph embedding techniques, systematically comparing these algorithms on data from diverse contexts and social media platforms. We demonstrate that both kinds of trust networks provide useful signals for estimating account credibility. Some of the proposed methods yield high accuracy, providing promising solutions to promote the dissemination of reliable information in online communities. Two kinds of homophily emerge from our results: accounts tend to have similar credibility if they reshare each other's content or share content from similar sources. Our methodology invites further investigation into the relationship between accounts and news sources to better characterize misinformation spreaders.
• The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape. Guo, Siyi; He, Zihao; Rao, Ashwin; Morstatter, Fred; Brantingham, Jeffrey; Lerman, Kristina (2024).
• Large Language Models for Data Annotation: A Survey. Tan, Zhen; Li, Dawei; Wang, Song; Beigi, Alimohammad; Jiang, Bohan; Bhattacharjee, Amrita; Karami, Mansooreh; Li, Jundong; Cheng, Lu; Liu, Huan (2024).
• Social Network and Semantic Analysis of Roe v. Wade’s Reversal on Twitter. Dai, Zehui; Higgs, Cory (2024). 42(1) 186–200.

  On June 24, 2022, the Supreme Court released its decision in Dobbs v. Jackson Women’s Health, which officially repealed Roe v. Wade and its subsequent rulings. Employing social network analysis and semantic analysis methods, the current project reviews the public reaction among Twitter users shared from the May 2 draft leak to the June 24 official repeal, using a series of Twitter hashtags related to Roe v. Wade. The project identified the main influencers within the network, namely, journalist/news organizations, Internet celebrities, activists/activist groups, professional/non-profit organizations, and politicians/political organizations through social network analysis. Through semantic analysis, the authors found prominent themes such as legal concerns, discourse on reproductive rights, distrusting of Supreme Court’s authority, and political nepotism. The results offer policy implications and communication message strategies to healthcare providers and policymakers. The authors believe that the polarizing nature of Roe v. Wade-related issues will be a crucial factor in shaping voters’ decisions during the upcoming 2024 presidential election.
• Measuring Online Emotional Reactions to Events. Guo, Siyi; He, Zihao; Rao, Ashwin; Jang, Eugene; Nan, Yuanfeixue; Morstatter, Fred; Brantingham, Jeffrey; Lerman, Kristina (2024).
• ValiText -- a unified validation framework for computational text-based measures of social constructs. Birkenmaier, Lukas; Wagner, Claudia; Lechner, Clemens (2024).
• Large Language Models Enable Few-Shot Clustering. Viswanathan, Vijay; Gashteovski, Kiril; Lawrence, Carolin; Wu, Tongshuang; Neubig, Graham (2024). 12 321–333.
• Embedding Elites: Examining the Use of Tweets Embedded in Online News Articles across Reliable and Fringe Outlets. Horne, Benjamin D.; Phillips, Summer; Koontz, Nelia (2024). abs/2401.16572
• Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning. Alizadeh, Meysam; Kubli, Maël; Samei, Zeynab; Dehghani, Shirin; Zahedivafa, Mohammadmasiha; Bermeo, Juan Diego; Korobeynikova, Maria; Gilardi, Fabrizio (2024).
• The new normal: Online political fandom and the co-opting of morals. Clue, Natalie Le (2024). 30(1) 347–357.

  This paper focuses on the under-studied and complex aspect of toxic fan practices by analysing the discourse surrounding the Twitter hashtags #CrookedHillary and #LockHerUp as used throughout the 2016 Presidential Election in the United States. Based on the analysis of these hashtags, the previously ‘normal’ and ‘acceptable’ type of discourse has been co-opted into what can, arguably, be termed as toxic rhetoric and fan behaviour on Twitter. The results of this paper point to an acceptance by fans of being immersed in toxic practices despite a paradigmatic relocation of what is ‘normal’ towards more unchecked, unethical, and toxic behaviour.
• Twitter’s pulse on hydrogen energy in 280 characters: a data perspective. Uniyal, Deepak; Nayak, Richi (2024). 14(1) 37.
• Remember the Human: A Systematic Review of Ethical Considerations in Reddit Research. Fiesler, Casey; Zimmer, Michael; Proferes, Nicholas; Gilbert, Sarah; Jones, Naiyan (2024). 8(GROUP) 1–33.

  Reddit is one of the world's most prominent social media platforms, and also a valuable source of data for internet researchers. However, working with this kind of data also presents novel ethical complications for researchers, including issues around privacy, vulnerable populations, and unintended consequences. This paper describes an analysis of 134 papers that rely on Reddit data while also including some discussion of ethical implications and/or considerations by the researchers. Our analysis of these papers reveals common ethical issues and ethically motivated methodological decisions, as described by the researchers themselves, while also exposing some gaps for further ethical contemplation for researchers relying on Reddit data. Based on these findings, we close with a set of recommendations for ethically-informed methods and reflection for researchers working with social data.
• War and mobility: Using Yandex web searches to characterize intentions to leave Russia after its invasion of Ukraine. Anastasiadou, Athina; Volgin, Artem; Leasure, Douglas R. (2024). 50 205–220.
• Chatbots Are Not Reliable Text Annotators. Kristensen-McLachlan, Ross Deans; Canavan, Miceal; Kardos, Márton; Jacobsen, Mia; Aarøe, Lene (2023).
• Performatively Archiving the Early Web: One Terabyte of Kilobyte Age. Bril, Marijn Josephien (2023). 12(23) 69–85.
• SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research. Antypas, Dimosthenis; Ushio, Asahi; Barbieri, Francesco; Neves, Leonardo; Rezaee, Kiamehr; Espinosa-Anke, Luis; Pei, Jiaxin; Camacho-Collados, Jose (2023).
• WordPPR: A Researcher-Driven Computational Keyword Selection Method for Text Data Retrieval from Digital Media. Yini Zhang, Fan Chen; Yue, Zhiying (2023). 1–17.
• Textual Indicators of Deliberative Dialogue: A Systematic Review of Methods for Studying the Quality of Online Dialogues. Goddard, Alex; Gillespie, Alex (2023). 41(6) 2364–2385.

  High-quality online dialogues help sustain democracy. Deliberative theory, which predates the internet, provides the primary model for assessing the quality of online dialogues. It conceptualizes high-quality online dialogue as civil, rational, constructive, equal, interactive, and for the common good. More recently, advances in computation have driven an upsurge of empirical studies using automated methods for operationalizing online dialogue and measuring its quality. While related in their aims, deliberative theory and the wider empirical literature generally operate independently. To bridge the gap between the two literatures, we introduce Textual Indicators of Deliberative Dialogue (TIDDs). TIDDs are defined as text-based measures of online dialogue quality under a deliberative model (e.g., disagreement, incivility, and justifications). In this study, we identified 123 TIDDs by systematically reviewing 67 empirical studies of online dialogue. We found them to have mid-low reliability, low criterion validity, and high construct validity for measuring two deliberative dimensions (civility and rationality). Our results highlight the limitations of deliberative theory for conceptualizing the variety of ways online dialogues can be operationalized. We report the most promising TIDDs for measuring the quality of online dialogue and suggest deliberative theory would benefit from altering its models in line with the broader empirical literature.
• Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias. Ungless, Eddie L.; Ross, Björn; Belle, Vaishak (2023). 41(6) 2211–2229.

  Automated sentiment analysis can help efficiently detect trends in patients’ moods, consumer preferences, political attitudes and more. Unfortunately, like many natural language processing techniques, sentiment analysis can show bias against marginalised groups. We illustrate this point by showing how six popular sentiment analysis tools respond to sentences about queer identities, expanding on existing work on gender, ethnicity and disability. We find evidence of bias against several marginalised queer identities, including in the two models from Google and Amazon that seem to have been subject to superficial debiasing. We conclude with guidance on selecting a sentiment analysis tool to minimise the risk of model bias skewing results.
• Comparing and mapping difference indices of debate quality on Twitter. Reveilhac, Maud (2023). 16(2) 234–249.

  Albeit the measurement of debate quality is not a new endeavour, this paper raises two research questions for which we still have limited knowledge: What are important and reliable indicators of debate quality on social media? How does debate quality relate to individual factors on social media? First, we empirically analysed how two well-established discourse’ quality indices (the DQI and CC index) correlate to each other using a random sample of 1000 tweets selected from the full history of tweets written by Swiss elected politicians between 2011 and 2021. While the sample was automatically coded for CC using LIWC, we manually annotated the tweets according to an adapted version of the DQI for social media texts. Second, we conducted a correspondence analysis to investigate the relations between these dimensions, additional debate quality features, as well as individual political factors. Results show a good correlation between both indices (r up to 0.46), while also highlighting their respective weaknesses. Furthermore, the results highlight the necessity to include alternative dimensions of debate quality (such as emotion and inclusive or exclusive views) to enhance future measurements of debate quality in the realm of social media.
• Comprehending Lexical and Affective Ontologies in the Demographically Diverse Spatial Social Media Discourse. Sazzed, Salim (2023).
• Unsupervised detection of coordinated fake-follower campaigns on social media. Zouzou, Yasser; Varol, Onur (2023).
• Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023): Workshop and Shared Task Report. Hürriyetoǧlu, Ali; Tanev, Hristo; Mutlu, Osman; Thapa, Surendrabikram; Tan, Fiona Anting; Yörük, Erdem (2023). abs/2312.01244
• The event-driven nature of online political hostility: How offline political events make online interactions more hostile. Hebbelstrup Rye Rasmussen, Stig; Petersen, Michael Bang (2023). 2(11) pgad382.

  Hostile interactions permeate political debates on social media, but what is driving the long-term developments in online political hostility? Prior research focuses on individual-level factors such as the dispositions of users or network-level factors such as echo chambers. Moving beyond these accounts, we develop and test an event-oriented explanation and demonstrate that over the course of the 2020 election year in the United States, all major shifts in political hostility on the social media platform Twitter were driven by external offline events. Importantly, these events were magnified by Twitter users within the most politically hostile and most ideologically homogeneous networks. Further contributing to the individual and network-oriented accounts, we show that divisive offline events mobilized individual users not already disposed for hostility and may have helped facilitate the formation of echo chambers. The dynamics of online interactions—including their level of hostility—seem crucially dependent on developments in the offline world.
• Construction of Domain-Specific Lexicons Based on Term Statistics. Rojas-Hernández, Rafael; Chau, Asdrúbal López; Valle-Cruz, David; Trujillo-Mora, Valentin; González-Jaimes, Elvira Ivone (2023). 1626–1631.
• Social cohesion in platformized public spheres: Toward a conceptual framework. Schneiders, Pascal; Stegmann, Daniel; Stark, Birgit (2023). 33(2-3) 122–131.
• OKG: A Knowledge Graph for Fine-grained Understanding of Social Media Discourse on Inequality. Blin, Inès; Stork, Lise; Spillner, Laura; Santagiustina, Carlo K. B. Venable, D. Garijo, B. Jalaian (eds.) (2023). 166–174.
• A Graph-Based Context-Aware Model to Understand Online Conversations. Agarwal, Vibhor; Young, Anthony P.; Joglekar, Sagar; Sastry, Nishanth (2023). 18(1) 1–27.

  Online forums that allow for participatory engagement between users have been transformative for the public discussion of many important issues. However, such conversations can sometimes escalate into full-blown exchanges of hate and misinformation. Existing approaches in natural language processing (NLP), such as deep learning models for classification tasks, use as inputs only a single comment or a pair of comments depending upon whether the task concerns the inference of properties of the individual comments or the replies between pairs of comments, respectively. However, in online conversations, comments and replies may be based on external context beyond the immediately relevant information that is input to the model. Therefore, being aware of the conversations’ surrounding contexts should improve the model’s performance for the inference task at hand.We propose GraphNLI,1 a novel graph-based deep learning architecture that uses graph walks to incorporate the wider context of a conversation in a principled manner. Specifically, a graph walk starts from a given comment and samples “nearby” comments in the same or parallel conversation threads, which results in additional embeddings that are aggregated together with the initial comment’s embedding. We then use these enriched embeddings for downstream NLP prediction tasks that are important for online conversations. We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection - and find that our model consistently outperforms all relevant baselines for both tasks. Specifically, GraphNLI with a biased root-seeking random walk performs with a macro-F1 score of 3 and 6 percentage points better than the best-performing BERT-based baselines for the polarity prediction and hate speech detection tasks, respectively. We also perform extensive ablative experiments and hyperparameter searches to understand the efficacy of GraphNLI. This demonstrates the potential of context-aware models to capture the global context along with the local context of online conversations for these two tasks.
• LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding. Chew, Robert; Bollenbacher, John; Wenger, Michael; Speer, Jessica; Kim, Annice (2023).
• Weak-PMLC: A large-scale framework for multi-label policy classification based on extremely weak supervision. Zhao, Jiufeng; Song, Rui; Yue, Chitao; Wang, Zhenxin; Xu, Hao (2023). 60(5) 103442.
• The expression of hate speech against Afro-descendant, Roma, and LGBTQ+ communities in YouTube comments. Carvalho, Paula; Caled, Danielle; Silva, Cláudia; Batista, Fernando; Ribeiro, Ricardo (2023).
• Characterizing controversiality of topics utilizing eccentricity of opinions. Pandey, Sriniwas; Sayama, Hiroki (2023). 5(1) 2.
• Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse. Rauniyar, Kritesh; Poudel, Sweta; Shiwakoti, Shuvam; Thapa, Surendrabikram; Rashid, Junaid; Kim, Jungeun; Imran, Muhammad; Naseem, Usman (2023). 11 143092–143115.
• Using the SP!CE Framework to Code Influence Campaign Activity on Social Media: Case Study on the 2022 Brazilian Presidential Election. Gocso, Alexander; Brito, Claudia Perez; Ruesca, Bryan; Mendes, Allen; Finlayson, Mark A. (2023).
• Dialing for Videos: A Random Sample of YouTube. McGrady, Ryan; Zheng, Kevin; Curran, Rebecca; Baumgartner, Jason; Zuckerman, Ethan (2023). 3
• Uncertainty Quantification for Text Classification. Zhang, Dell; Sensoy, Murat; Makrehchi, Masoud; Taneva-Popova, Bilyana in Lecture Notes in Computer Science, J. Kamps, L. Goeuriot, F. Crestani, M. Maistro, H. Joho, B. Davis, C. Gurrin, U. Kruschwitz, A. Caputo (eds.) (2023). (Vol. 13982) 362–369.
• Validating Digital Traces with Survey Data: The Use Case of Religiosity. Kına, M. Fuat; Yörük, Erdem; Hürriyetoğlu, Ali; Yardı, Melih Can; Atsızelti, Şükrü; Duruşan, Fırat; Gürerk, Oğuz; Etgü, Tolga; Nişancı, Zübeyir; Mutlu, Osman; Turbic, Gizem Bacaksızlar; Akbulut, Yusuf (2023).
• A corpus-assisted critical discourse analysis of hate speech in German and Polish social media posts. Jaszczyk-Grzyb, Magdalena; Szczepaniak-Kozak, Anna; Adamczak-Krysztofowicz, Sylwia (2023). 117(1) 44–71.
• Misogyny, Women in Power, and Patterns of Social Media Harassment. Golbeck, Jennifer in Lecture Notes in Computer Science, R. Thomson, S. Al khateeb, A. Burger, P. S. Park, A. A. Pyke (eds.) (2023). (Vol. 14161) 3–11.
• Silence Speaks Volumes: Re-weighting Techniques for Under-Represented Users in Fake News Detection. Karami, Mansooreh; Mosallanezhad, David; Sheth, Paras; Liu, Huan (2023).
• A language framework for modeling social media account behavior. Nwala, Alexander C.; Flammini, Alessandro; Menczer, Filippo (2023). 12(1)
• On Methods of Data Standardization of German Social Media Comments. Melnyk, Lidiia; Feld, Linda (2023).
• NeMig -- A Bilingual News Collection and Knowledge Graph about Migration. Iana, Andreea; Alam, Mehwish; Grote, Alexander; Nikolajevic, Nevena; Ludwig, Katharina; Müller, Philipp; Weinhardt, Christof; Paulheim, Heiko (2023).
• A Workflow to Detect, Monitor, and Update Lists of Coordinated Social Media Accounts Across Time: The Case of the 2022 Italian Election. Giglietto, Fabio; Marino, Giada; Mincigrucci, Roberto; Stanziano, Anna (2023). 9(3) 20563051231196866.

  Information operations that target public opinion often exploit breaking news, crises, and elections by using coordinated social media actors to disseminate problematic content. These events often reveal the relationships between actors, prompting the creation of lists of malicious actors and news sources. However, relying on outdated lists may underestimate the prevalence and impact of such operations. This article presents a novel workflow to detect, monitor, and update lists of coordinated social media actors during and beyond peak activity periods. Using this approach, known problematic actors are constantly monitored, allowing the detection of new actors and the update of the monitored pool. The workflow was applied to the 2022 Italian snap election, leveraging previous research on coordinated inauthentic behavior during the 2018 and 2019 Italian elections. The initial list of 435 coordinated accounts was monitored, surfacing 1,022 overly shared or commented political posts, 272 coordinated links, and detecting 66 political and 554 generic coordinated accounts not previously listed. Three case studies were identified: one politically motivated, one click-economy driven, and one religiously motivated operation. The article discusses the implications of this approach, its limitations, and potential future work.
• Artificially Intelligent Opinion Polling. Cerina, Roberto; Duch, Raymond (2023).
• Political Disaffection in the Digital Age: The Use of Social Media and the Gap in Internal and External Efficacy. Fierro, Pedro; Aroca, Patricio; Navia, Patricio (2023). 41(5) 1857–1876.

  This work analyzes the relationship between social media use and the development of internal and external political efficacy, using five 1,650-person polls conducted in 2017–2021 in the 10 most populated municipalities of in the Valparaiso Region in Chile, a country characterized by high levels of political discontent and social protests, especially in 2019. With Structural Equation Models, we report positive impacts of different magnitudes of the civic use of social media on internal and external efficacy and no effect of the non-civic use of social media on either type of efficacy. As lower internal and external political efficacy feeds political disaffection, the differentiated use of social media can deepen the gap between engaged and disaffected citizens.
• Polarization of Opinions on COVID-19 Measures: Integrating Twitter and Survey Data. Reiter-Haas, Markus; Klösch, Beate; Hadler, Markus; Lex, Elisabeth (2023). 41(5) 1811–1835.

  Polarization of public opinion is a major issue for societies, as high levels can promote adverse effects such as hostility. The present paper focuses on the polarization of opinions regarding COVID-19 prevention measures in survey data and on Twitter in the German-speaking regions of Germany, Austria, and Switzerland. The level of polarization is measured by dispersion and bimodality in the opinions based on the sentiment in Twitter data and the agreement in the survey data. Our paper, however, goes beyond existing research as we consider data from both sources separately and comparatively. For this purpose, we matched individuals’ survey responses and tweets for those respondents who shared their Twitter account information. The analyses show that vaccination is more polarizing compared to mask wearing and contact tracing in both sources, that polarization of opinions is more pronounced in the survey data compared to the Twitter data, but also that individuals’ opinions about the COVID-19 measures are consistent in both sources. We believe our findings will provide valuable insights for integrating survey data and Twitter data to investigate opinion polarization.
• A Comparative Study of Bot Detection Techniques With an Application in Twitter Covid-19 Discourse. Antenore, Marzia; Rodriguez, Jose Manuel Camacho; Panizzi, Emanuele (2023). 41(5) 1520–1545.

  Bot Detection is crucial in a world where Online Social Networks (OSNs) play a pivotal role in our lives as public communication channels. This task becomes highly relevant in crises like the Covid-19 pandemic when there is a growing risk of proliferation of automated accounts designed to produce misinformation content. To address this issue, we first introduce a comparison between supervised Bot Detection models using Data Selection. The techniques used to develop the bot detection models use features such as the tweets’ metadata or accounts’ Digital Fingerprint. The techniques implemented in this work proved effective in detecting bots with different behaviors. Social Fingerprint-based methods have been found to be effective with bots that behave in a coordinated manner. Furthermore, all these approaches have produced excellent results compared to the Botometer v3. Second, we present and discuss a case study related to the Covid-19 pandemic that analyses the differences in the discourse between bots and humans on Twitter, a platform used worldwide to express opinions and engage in dialogue in a public arena. While bots and humans generally express themselves alike, the tweets’ content and sentiment analysis reveal some dissimilitudes, especially in tweets concerning President Trump. When the discourse switches to pandemic management by Trump, sentiment-related values display a drastic difference, showing that tweets generated by bots have a predominantly negative attitude. However, according to our findings, while automated accounts are numerous and active in discussing controversial issues, they usually do not seem to increase exposure to negative and inflammatory content for human users.
• Integrated content-network analysis to discover influential collectives for studying social cyber-threats from online social movements. Amro, Falah; Purohit, Hemant (2023). 13(1) 120.

  Recent online social movements with offline violence events have been linked to activities of threat actors that exploit online social networks to instigate violence, among other ills. In recent years, researchers have begun to explore natural language processing (NLP) and social network analysis (SNA) tools in a myriad of applications for early detection, forecasting, and prevention of social cyber-threat actors to help preserve public safety. NLP-based approaches provide a promising direction to characterize the behavioral patterns of actors in online social movements that facilitate mechanisms for early detection of social cyber-threat actors. Similarly, SNA-based approaches provide tools to study structural patterns for actor interaction and measure different properties of the social network of participating actors in online social movements. However, tools designed using the foundation of an isolated approach of either NLP or SNA are insufficient to uncover patterns of connective and collective actions from the information disseminated during violent social movements, which could help identify the collective and connective set of influential actors behind the violent events in these movements. This paper proposes a novel integrated content-network analysis approach to combine NLP and SNA techniques to monitor actor interactions in social networks, which, in turn, enables us to identify actors of interest as an emerging social identity group and map their collective influence at different points in time. A mixed-method analysis using our approach on the dataset of a recent online social movement of ``Freedom Convoy'' in Canada shows that an influential group of actors, referred to here as, influential collective, generated aggressive and emotional content to influence others in developing coordinated activities of manipulation and instigation. We show that group centrality measures of SNA can be leveraged to discover the influential collective that is the primary driver to sustain the online social movement in the days leading up to the protest and during escalations. Using a variety of centrality measures, we examine how the influential collective both maintained its influence over time and increased it substantially in the peak days of the social movement. The resulting insights from the application of our analytical approach of integrating content and network analyses present opportunities for first responder agencies in the detection, monitoring, and prevention of offline violent activities instigated by online social movements as they unfold in real time, as well as inform how implicit social identities could be formed because of the emergence of such influential collectives.
• Quantifying participation biases on social media. Pokhriyal, Neeti; Valentino, Benjamin A.; Vosoughi, Soroush (2023). 12(1)
• From alternative conceptions of honesty to alternative facts in communications by US politicians. Lasser, Jana; Aroyehun, Segun T.; Carrella, Fabio; Simchon, Almog; Garcia, David; Lewandowsky, Stephan (2023). 7(12) 2140–2151.

  The spread of online misinformation on social media is increasingly perceived as a problem for societal cohesion and democracy. The role of political leaders in this process has attracted less research attention, even though politicians who `speak their mind' are perceived by segments of the public as authentic and honest even if their statements are unsupported by evidence. By analysing communications by members of the US Congress on Twitter between 2011 and 2022, we show that politicians' conception of honesty has undergone a distinct shift, with authentic belief speaking that may be decoupled from evidence becoming more prominent and more differentiated from explicitly evidence-based fact speaking. We show that for Republicans---but not Democrats---an increase in belief speaking of 10% is associated with a decrease of 12.8 points of quality (NewsGuard scoring system) in the sources shared in a tweet. In contrast, an increase in fact-speaking language is associated with an increase in quality of sources for both parties. Our study is observational and cannot support causal inferences. However, our results are consistent with the hypothesis that the current dissemination of misinformation in political discourse is linked to an alternative understanding of truth and honesty that emphasizes invocation of subjective belief at the expense of reliance on evidence.
• Enhancing prediction of user stance for social networks rumors. Khaled, Kholoud; ElKorany, Abeer; Ezzat, Cherry A. (2023).
• Platform-controlled social media APIs threaten open science. Davidson, Brittany I.; Wischerath, Darja; Racek, Daniel; Parry, Douglas A.; Godwin, Emily; Hinds, Joanne; van der Linden, Dirk; Roscoe, Jonathan F.; Ayravainen, Laura; Cork, Alicia G. (2023). 7(12) 2054–2057.

  Social media data enable insights into human behaviour. Researchers can access these data via platform-provided application programming interfaces (APIs), but these come with restrictive usage terms that mean studies cannot be reproduced or replicated. Platform-owned APIs hinder access, transparency and scientific knowledge.
• Like-minded sources on Facebook are prevalent but not polarizing. Nyhan, Brendan; Settle, Jaime; Thorson, Emily; Wojcieszak, Magdalena; Barberá, Pablo; Chen, Annie Y.; Allcott, Hunt; Brown, Taylor; Crespo-Tenorio, Adriana; Dimmery, Drew; Freelon, Deen; Gentzkow, Matthew; González-Bailón, Sandra; Guess, Andrew M.; Kennedy, Edward; Kim, Young Mie; Lazer, David; Malhotra, Neil; Moehler, Devra; Pan, Jennifer; Thomas, Daniel Robert; Tromble, Rebekah; Rivera, Carlos Velasco; Wilkins, Arjun; Xiong, Beixian; de Jonge, Chad Kiewiet; Franco, Annie; Mason, Winter; Stroud, Natalie Jomini; Tucker, Joshua A. (2023). 620(7972) 137–144.

  Many critics raise concerns about the prevalence of `echo chambers' on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from `like-minded' sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.
• Domain-adaptive pre-training on a BERT model for the automatic detection of misogynistic tweets in Spanish. Rodríguez, Dalia A.; Diaz-Escobar, Julia; Díaz-Ramírez, Arnoldo; Trujillo, Leonardo (2023). 13(1) 126.

  Violence against women is a major social issue. One in every three women worldwide has been subjected to physical or sexual violence. The pervasive violence against women in the physical world, the ever-growing presence of social media in our lives, and its lack of content moderation have led to an influx of misogynistic social media content. We contribute to preventing violence against women by introducing a BERT architecture with domain-adaptive pre-training to detect misogynistic tweets in Spanish automatically. We used the IbeEval 2018 Spanish dataset for automatic misogyny identification, obtaining an accuracy of 84.60%, precision of 79.64%, recall at 86.70%, and F-1 score of 83.02%, outperforming the state of the art. We also conducted a manual error analysis and discovered 469 mislabeled tweets and a misogynistic bias in the IbeEval 2018 Spanish dataset. Our debiased model outperformed the current literature on automatic misogyny detection with an accuracy of 84.35%, precision of 84.64%, recall of 83.93%, and F-1 score of 84.28%. Lastly, we addressed the need for misogyny detection on other social media by experimenting with a manually curated and labeled dataset of Facebook comments in Spanish for automatic misogyny detection. We obtained excellent results with an accuracy of 87.85%. Misogyny is a complex social issue, so an interdisciplinary approach might benefit future models for automatically detecting misogyny.
• Text mining of veterinary forums for epidemiological surveillance supplementation. Munaf, Samuel; Swingler, Kevin; Brülisauer, Franz; O’Hare, Anthony; Gunn, George; Reeves, Aaron (2023). 13(1) 121.

  Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.
• Tracking the structure and sentiment of vaccination discussions on Mumsnet. Silva, Miguel E. P.; Skeva, Rigina; House, Thomas; Jay, Caroline (2023). 13(1) 152.

  Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a UK-based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets---small connected network subgraphs---we show how the topological structure of vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment toward vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis.
• Social network dynamics, bots, and community-based online misinformation spread: Lessons from anti-refugee and COVID-19 misinformation cases. Zhen, Lichen (2023). 39(1) 17–34.
• Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets. Low, Jwen Fai; Fung, Benjamin C. M.; Iqbal, Farkhund (2023). 11 87009–87033.

  COVID-19 is an opportunity to study public acceptance of a “new” healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, and controversiality and disagreeableness, represented by ratios of the aforementioned counts, favored pro-mask tweets initially but with anti-mask tweets slowly gaining ground. Additional analysis raised the possibility of the platform owners suppressing certain parts of the mask-wearing discussion.
• Measuring Online Emotional Reactions to Events. Guo, Siyi; He, Zihao; Rao, Ashwin; Jang, Eugene; Nan, Yuanfeixue; Morstatter, Fred; Brantingham, P. Jeffrey; Lerman, Kristina B. A. Prakash, D. Wang, T. Weninger (eds.) (2023). 315–319.
• ChatGPT outperforms crowd workers for text-annotation tasks. Gilardi, Fabrizio; Alizadeh, Meysam; Kubli, Maël (2023). 120(30) e2305016120.
• Discovering collective narratives shifts in online discussions. Zhao, Wanying; Guo, Siyi; Lerman, Kristina; Ahn, Yong-Yeol (2023).
• How to handle web archive versions?. NielsBr¨ugger, ∗; Clavert, Fr´ed´eric; JoshgunSirajzade, ∗; KarinDeWild, ∗ (2023).
• Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media. Bucur, Ana-Maria (2023).
• Radical Mobilization in Times of Crisis: Use and Effects of Appeals and Populist Communication Features in Telegram Channels. Jost, Pablo; Dogruel, Leyla (2023). 9(3) 20563051231186372.

  Social crisis situations such as the COVID-19 pandemic provide a fertile ground for radical actors and social movements to strengthen their radical mobilization—mainly using social media. In light of the deplatforming efforts of social media platforms that forced radical actors to establish new channels to continue their discourses and the recent crisis that opened a discursive opportunity structure, we investigate how radical actors use Telegram for mobilization. Based on a longitudinal manual content analysis of 13,371 messages from 188 German Telegram channels of the Querdenken movement, conspiracy, and far-right actors, we examine mobilization strategies (namely direct appeals and populist blame game) and their success (retransmission) between March 2020 and December 2021. We found that both offline and online appeals and the use of anti-elitism increased significantly over time. The use of direct and indirect mobilization attempts is related to the ideological background of the actors, with direct appeals being favored by the Querdenken movement. Furthermore, the results shed light on the mechanisms of radical mobilization on Telegram, showing that both direct appeals and the populist blame game are successful strategies for radical actors because they increase message retransmission.
• Fulmqa: a fuzzy logic-based model for social media data quality assessment. Reda, Oumaima; Zellou, Ahmed (2023). 13(1) 150.

  Nowadays, with the advancement of information technology and the growing importance of social media, social media platforms such as Twitter and Facebook have become deeply entrenched in our lives and are growing rapidly around the world. On these platforms, users have the freedom to share and publish whatever they want without control, leading to faster generation and dissemination of information, as a result, rumors and false information can then be spread and seen by a larger number of people. Consequently, assessing the quality of these data proves to be of major importance. In this paper, we aim to present a new and efficient quality assessment model including the quality metrics needed for an accurate assessment. Our work presents an extension of the SMDQM (Social Media Data Quality Model) (Reda and Zellou, in: International conference on innovative research in applied science, engineering and technology, IEEE, 2022), which is used to assess the quality of data provided by social media platforms. We suggest using fuzzy logic to describe data quality metrics in order to overcome imprecision and subjectivity. Next, we perform extensive experiments to evaluate the performance of our model using a real-world implementation by performing an evaluation on two separate Twitter data sets. The findings indicate that the model can successfully evaluate all tweets with high performance.
• Online Incivility in the 2020 Congressional Elections. Heseltine, Michael; Dorsey, Spencer (2022). 75(2) 512–526.

  This paper explores the prevalence and correlates of political incivility among Congressional candidates in the 2020 election cycle, focusing specifically on which types of candidates were most likely to use uncivil language in their online communications and the self-reinforcing nature of incivility between candidates. Based on a comprehensive analysis of more than two million tweets sent by major party candidates in the 2020 House and Senate races, we conclude that several individual and electoral factors were influential in driving candidate incivility. Specifically, Republicans, challengers, and candidates in less competitive races were more likely to use uncivil rhetoric. Women, racial minorities, and candidates running in open seat races were less prone to incivility. We also find that incivility begets incivility, with candidates whose opponents used higher rates of incivility also being more likely to use incivility themselves. Uncivil tweets were also found to generate significantly more likes and retweets, suggesting that incivility is a viable means of driving engagement for candidates. These results shed light on the factors behind incivility among political elites, as well as highlight the feedback effects which contribute to a self-reinforcing rise in political incivility.
• Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data. Bianchi, Federico; Cutrona, Vincenzo; Hovy, Dirk W. Che, E. Shutova (eds.) (2022). 289–297.
• Structural Scaffolding for Sensemaking in Document Collections Barrow, Joseph (2022). University of Maryland, College Park.
• Mapping out Violence Against Women of Influence on Twitter Using the Cyber–Lifestyle Routine Activity Theory. Kumar, Priya; Gruzd, Anatoliy; Mai, Philip (2021). 65(5) 689–711.

  The study applies and expands the routine activity theory to examine the dynamics of online harassment and violence against women on Twitter in India. We collected 931,363 public tweets (original posts and replies) over a period of 1 month that mentioned at least one of 101 influential women in India. By undertaking both manual and automated text analysis of “hateful” tweets, we identified three broad types of violence experienced by women of influence on Twitter: dismissive insults, ethnoreligious slurs, and gendered sexual harassment. The analysis also revealed different types of individually motivated offenders: “news junkies,” “Bollywood fanatics,” and “lone-wolves”, who do not characteristically engage in direct targeted attacks against a single person. Finally, we question the effectiveness of Twitter’s form of “guardianship” against online violence against women, as we found that a year after our initial data collection in 2017, only 22% of hostile posts with explicit forms of harassment have been deleted. We conclude that in the social media age, online and offline public spheres overlap and intertwine, requiring improved regulatory approaches, policies, and moderation tools of “capable” guardianship that empower women to actively participate in public life.
• Obesity treatment: Weight loss versus increasing fitness and physical activity for reducing health risks. Gaesser, Glenn A.; Angadi, Siddhartha S. (2021). 24(10) 102995.
• Local Media and Geo-situated Responses to Brexit: A Quantitative Analysis of Twitter, News and Survey Data. Gorrell, Genevieve; Bakir, Mehmet E.; Temple, Luke; Maynard, Diana; Cifariello, Paolo; Harrison, Jackie; Kanai, J. Miguel; Bontcheva, Kalina (2020).
• After the ‘APIcalypse’: social media platforms and their fight against critical scholarly research. Bruns, Axel (2019). 22(11) 1544–1566.
• Hate Speech Detection is Not as Easy as You May Think: A Closer Look at Model Validation. Arango, Aymé; Pérez, Jorge; Poblete, Barbara B. Piwowarski, M. Chevalier, Éric Gaussier, Y. Maarek, J.-Y. Nie, F. Scholer (eds.) (2019). 45–54.
• Revisiting Computational Models of Argument Schemes: Classification, Annotation, Comparison. Visser, Jacky; Lawrence, John; Wagemans, Jean H. M.; Reed, Chris in Frontiers in Artificial Intelligence and Applications, S. Modgil, K. Budzynska, J. Lawrence (eds.) (2018). (Vol. 305) 313–324.
• Revisiting Computational Models of Argument Schemes: Classification, Annotation, Comparison. Visser, Jacky; Lawrence, John; Wagemans, Jean H. M.; Reed, Chris (2018).
• Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis. Garten, Justin; Hoover, Joe; Johnson, Kate M.; Boghrati, Reihane; Iskiwitch, Carol; Dehghani, Morteza (2018). 50(1) 344–361.

  Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience.
• What’s Wrong With the ‘War on Obesity?’ A Narrative Review of the Weight-Centered Health Paradigm and Development of the 3C Framework to Build Critical Competency for a Paradigm Shift. O’Hara, Lily; Taylor, Jane (2018). 8(2) 215824401877288.
• A Collaborative Approach to Research Data Management in a Web Archive Context. Huurdeman, Hugo C.; Kamps, Jaap F. Kruse, J. B. Thestrup (eds.) (2017). 55–78.

  In our times, researchers create and gather their datasets, irrevocably changing from solely “small” to increasingly “big” data. Institutions provide more and more services to manage, store and access this essential research data. However, despite the invaluable services provided by repositories, there is still a mismatch between the myriad of operations carried out by researchers, and the storage services offered by generalized repositories: often, only the end products of the research process are stored. This may mean that valuable operations and transformations of data during the research process are lost, in effect also making it harder for future researchers to interpret this data. To address this issue, and to bring researchers and institutions closer together, we propose a more collaborative approach, potentially involving researchers in their entire workflow, from data creation to use and potential reuse. This proposition is contextualized via the concrete use case of the web archive. Organizations and individuals across the globe archive web content, assembled into vast web archives. These web archives could potentially be used as research datasets in various settings, ranging from computer science to the humanities. However, they have scarcely been used for research thus far, due to a number of limitations, including suboptimal access services for research. This chapter discusses the experiences and lessons learned in the concrete case of the web archive, and their implications for research data management at large.
• Validity of claims made in weight management research: a narrative review of dietetic articles. Aphramor, Lucy (2010). 9(1) 30.

  The best available evidence demonstrates that conventional weight management has a high long-term failure rate. The ethical implications of continued reliance on an energy deficit approach to weight management are under-explored.
• Have American’s Social Attitudes Become More Polarized?. DiMaggio, Paul; Evans, John; Bryson, Bethany (1996). 102(3) 690–755.

  Many observers have asserted with little evidence that Americans' social opinions have become polarized. Using General Social Survey and National Election Survey social attitude items that have been repeated regularly over 20 years, the authors ask (1) Have Americans' opinions become more dispersed (higher variance)? (2) Have distributions become flatter or more bimodal (declining kurtosis)? (3) Have opinions become more ideologically constrained within and across opinion domains? (4) Have paired social groups become more different in their opinions? The authors find little evidence of polarization over the past two decades, with attitudes toward abortion and opinion differences between Republican and Democratic party identifiers the exceptional cases.