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This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

Based on a lexical analysis of publications on 529 Facebook pages, published between 2013 and 2017, this research explores how Brazilian left-wing activist groups participate on Facebook to coordinate their opposition and engage in social struggles. This chapter shows how activist groups set up two main digital network repertoires of action when mobilizing on Facebook. First, in direct connection with major political events, the platform is used as a media arena to challenge governments’ political actions and second, it is employed as a tool to coordinate mobilization, whether these mobilizations are demonstrations on the street or at cultural events, such as at a music concert. These repertoires of action exemplify ways in which contemporary Brazilian activism is carried out at the intersection of online and offline engagements. While participants engage through these two repertoires, this network of activists is held together over time through a more mundane type of event, pertaining to the repertoire of action allowing the organization of mobilization. Stepping aside from opposition and struggles brought to the streets, the organization of cultural activities, such as concerts and exhibitions, punctuates the everyday exchanges in activists’ communications. Talk about cultural events and their related social agendas structures activist networks on a medium-term basis and creates the conditions for the coordination of (future) social movements, in that they offer the opportunities to stay in contact, in addition to taking part in occasional gatherings, between more highly visible social protests.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

This study aims to analyze corporate mail and wire fraud penalties, using bounded rationality in decision-making and assessing internal and external influences on prosecutorial choices.

The study analyzed 467 cases from 1992 to 2019, using data from the Corporate Prosecution Registry of the University of Virginia School of Law and Duke University School of Law. It examined corporations facing mail and wire fraud charges and other fraud crimes. Multiple regression linked predictor variables to the dependent variable, total payment.

The study found that corporate penalties tend to be lower for financial institutions or corporations in countries with US free trade agreements. Conversely, penalties are higher when the company is a U.S. public company or filed in districts with more pending criminal cases.

This study’s originality lies in applying the bounded rationality model to assess corporate prosecutorial decisions, unveiling external factors’ influence on corporate penalties.

This study aims to investigate the decline of American hegemony as one of the most prominent crises of the modern world order, from a broader perspective that transcends narrow traditional interpretations. The paper assumes that the September 11 events in 2001 have launched the actual decline in American hegemony. Tracing the evolution of US global strategy over the past two decades, the study seeks to analyze the main causes and repercussions of the decline of US hegemony, which would provide a bird’s eye view of what the current global system is going through.

The study investigates the decline in American hegemony through a longitudinal within-case analysis which focuses on the causal path of decline in hegemony in the case of the USA, since the events of September 11, 2001, and tries to identify the causal mechanisms behind this decline. Following George and Bennet (2005), the study uses process tracing to examine its research question. Process-tracing method seeks to identify the intervening causal process – causal chain or causal mechanisms or the steps in a causal process – that leads to the outcome of a particular case in a specific historical context (Mahoney, 2000; Bennet and Elman, 2006). The study chose this method, as it offers more potential for identifying causal mechanisms and theory testing (George and Bennet, 2005); it opted for a specific procedure, among the variety of process-tracing procedures listed by George and Bennet, which is the detailed narrative presented as a chronicle, accompanied by explicit causal hypotheses. Using this process tracing procedure, the study assumes that American hegemony has witnessed dramatic changes in the aftermath of critical junctures, particularly the events of September 11, 2001, and the financial crises, 2008, which contributed significantly to this decline. Consequently, it traces the impact of these events on the state of American hegemony, in light of the review of contributions of different theories on hegemony in the field of international relations, both traditional and critical. Consequently, introducing the theoretical framework used in the study (the four-dimensional model of hegemony), which transcends criticisms of previous theories.

The crises of the modern world order and the decline of American hegemony – being the main manifestation of such crises – revealed the inability of the traditional and critical approaches reviewed in the study to interpret this decline and those crises. The reason behind that was the inability of these interpretations to reflect the various dimensions of American hegemony and its decline since the September 11 events. This highlights the importance of using the four-dimensional model, which combines different factors in the analysis and has proved to be an appropriate model for studying the case of American hegemony and its decline after the events of September 11, as it deals with the phenomenon of hegemony as a social relationship based on specific social networks.

Despite the currency and relevance of the decline of US hegemony for both the academic and political world, the topic needed to be analyzed systemically and addressed in a thorough scientific way. Through the application of theoretical concepts into the analysis of empirical data, this study contributes to a field where too often the discourse about decline of American hegemony is led without the required theoretical or conceptual considerations.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.