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Whenever social injustice tackled by social movements receives heightened media attention, charitable crowdfunding platforms offer an opportunity to proactively advocate for equality by donating money to affected people. This research examines how the Black Lives Matter movement and the associated social protest cycle after the death of George Floyd have influenced donation behavior for campaigns with a personal goal and those with a societal goal supporting the black community.

This paper follows a quantitative research approach by applying a quasi-experimental research design on a GoFundMe dataset. In total, 67,905 campaigns and 1,362,499 individual donations were analyzed.

We uncover a rise in donations for campaigns supporting the black community, which lasts substantially longer for campaigns with a societal than with a personal funding goal. Informed by construal level theory, we attribute this heterogeneity to changes in the level of abstractness of the problems that social movements aim to tackle.

This research advances the knowledge of individual donation behavior in charitable crowdfunding. Our results highlight the important role that charitable crowdfunding campaigns play in promoting social justice and anti-discrimination as part of social protest cycles.

The slow food movement has become increasingly widespread globally in recent years. This paper focuses on explaining how Turkish cuisine, which has a deep-rooted history, meshes with the slow food movement and how this movement affects consumer behavior.

Based on expert opinion analysis with academics knowledgeable about the food industry and gastronomy, this paper explores how the slow food movement in Turkiye is evolving and how consumers perceive it. Content analysis was applied to the data obtained from the personal interviews.

The authors find that the slow food movement creates a strong brand image for businesses that rely on emphasizing the responsibility to the ecological system while appealing to the five senses of consumers. It already shows great potential even in emerging markets where typical household discretionary income is modest.

Based on key theories regarding all sales activism cases, the authors have offered insights into the dynamics, motivations and techniques of the case. Ensuring the preservation of the slow food movement, framing and creating associations need to be examined.

Slow food is a movement that emerged against the standard, fast, tasty, but unhealthy products of the fast-food industry. It entails product variety, local flavors and preference for the single-flavor focus embedded in the fast-food movement. The movement started with considerations of gastronomy and later was institutionalized as a social movement phenomenon. Later, it expanded its base to activism, targeting various social issues.

National surveys reveal that sports fans exhibit greater support for athletes, sports teams and leagues endorsing social justice initiatives compared to the general population, highlighting the potential of sports for positive social impact. This study investigates whether such responses are influenced by systematic biases.

Replicating a Nielsen national survey, two experiments explore whether biases affect support for athletes' participation in the Black Lives Matter (BLM) movement. The study also examines partisan motivated reasoning as a factor driving sports fans' support for BLM.

While avid fans display stronger endorsement of BLM compared to causal/non-sports fans, evidence suggests that systematic biases distort these responses. When sport identity becomes salient, reported support for the BLM movement becomes inflated.

Researchers often employ self-report surveys to gauge audience perceptions of athlete activism or cause-related initiatives, particularly when assessing their impact. This study's findings indicate that this context is susceptible to SDB.

The study underscores the role of systematic biases in self-report surveys, particularly in socially desirable contexts. People tend to over-report “positive behavior,” leading survey participants to respond more favorably to questions that are socially desirable. Therefore, interpreting survey results with caution becomes essential when the research context is deemed socially (un)desirable. It is crucial for researchers to apply appropriate measures to identify and mitigate systematic response biases. The authors recommend that researchers adopt both procedural and statistical remedies to detect and reduce social desirability biases.

This opening chapter of the edited volume, Childhood, Youth and Activism: Demands for Rights and Justice from Young People and Their Advocates, explores activism and advocacy – by and for children and young people. It begins by considering how activism has been understood in the scholarly literature, before making a case for a broad and inclusive conceptualisation of what counts as this particular form of social action. Relatedly, it examines the contours of the relationship between activism and advocacy, drawing attention to the ways in which these concepts converge, an issue that is particularly salient when applied to the categories of child and youth. Themes that emerge in research on child and youth activism are then drawn out and we identify some of the key issues that animate this work across various disciplines. These include observations that young people have long been central to social movements, the role of social media in youth activism, the nature of child and adult relationships in social movement organisations, and some of the issues that arise for young activists in relation to intersectional identities. To this we add debates regarding the politics of recognition, questions of voice and agency, and responsibility and their temporal registers. This discussion also foreshadows themes that emerge in the chapters across this volume. Finally, we offer a reflection on some of the conceptual issues raised when considering the book in its entirety, including those of voice, responsibility for the future, the politics of possibility and hope, and the many different forms and practices that activism and advocacy for and by young people take.

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.