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Given the historical legacy of policing Black bodies, this research focuses on the structures of anti-Blackness within school policing and the strategies students of Color activists use as they work to defund or abolish police departments in the Los Angeles Unified School District (LAUSD).

Specifically, this article looks to Twitter as a counter-storytelling space for students of Color activists to organize and build movements to end anti-Black school policing. Through the frameworks of critical race theory (CRT) and Black critical theory (BlackCrit), this research applies inductive coding to analyze 42 Twitter posts from three students of Color-led organizations based in Los Angeles.

This document analysis presents four themes, which describe four dominant strategies students of Color activists use in their campaigns to defund or abolish school police in the LAUSD: (1) centering Blackness and Black student experiences, (2) making demands for the elimination of funding and support for school police, (3) calling for a shift in funding to support Black students and (4) employing multiple tactics concurrently.

These findings demonstrate the importance of developing and centering a critical understanding of anti-Blackness to achieve racial and educational justice within social movements.

Moreover, the demands of students of Color activists reflect visions of public schools free from anti-Black school policing.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

This study uses a comprehensive theoretical framework that combines social cognitive theory and neighborhood effect to investigate the influence of neighborhood effects on farmers’ outcome expectations, observational learning and self-efficacy. This study aims is to analyze the mechanisms that underlie the adoption of social media by farmers for knowledge exchange in the agricultural context. Specifically, this research explores the role of neighborhood effects, outcome expectations, observational learning and self-efficacy in shaping farmers’ decision-making process regarding the use of social media platforms for exchanging agricultural knowledge.

The study data was collected through a sample survey conducted among 570 agricultural households residing in the provinces of Thai Nguyen, Cao Bang, Bac Kan and Phu Tho, located in the northern region of Vietnam. To analyze the data, structural equation modeling was used as the statistical technique of choice.

The findings of the study indicate a significant influence of neighborhood effects on outcome expectations, observational learning and self-efficacy. These factors, derived from social cognitive theory, also exhibit a positive association with farmers’ adoption of social media for knowledge exchange. Additionally, the study highlights that neighborhood contribute to a favorable adoption of social media among farmers via outcome expectations, observational learning, and self-efficacy.

The study is limited in examining farmers’ social media adoption for agriculture knowledge exchange in Northern mountainous area of Vietnam. This study could be replicated across various regions or nations, providing comparative insights into the adoption of social media among farmers for knowledge exchange.

The study findings suggest practical and innovative means to promote farmers’ social media adoption for agriculture knowledge exchange.

This study presents a pioneering approach by integrating social cognitive theory and neighborhood effect to elucidate the factors influencing farmers’ adoption of social media for the purpose of agriculture knowledge exchange.

This paper aims to examine how and why consumers respond to chief executive officer (CEO) activism on social media. The authors developed a conceptual model that proposes impression management as a mechanism for consumer response to CEO activism.

In Study 1a, the authors examined 83,259 tweets from 90 CEOs and compared consumer responses between controversial and noncontroversial tweets. In Study 1b, the authors replicated the analysis, using a machine-learning topic modeling approach. In Studies 2 and 3, the authors used experimental designs to test the theoretical mechanism.

On average, consumers tend to respond more to CEO posts dealing with noncontroversial issues. Consumers’ relative reluctance to like and share controversial posts is motivated by fear of rejection. However, CEO fame reverses this effect. Consumers are more likely to engage in controversial activist threads by popular CEOs. This effect holds for consumers high (vs low) in public self-consciousness. CEO fame serves as a “shield” behind which consumers protect their online image.

The study focused on Twitter (aka “X”) in the USA. Future research may replicate the study in other social media platforms and countries. The authors introduce “shielding” – liking and sharing content authored by a recognizable source – as a tactic for impression management on social media.

Famous CEOs should speak up about controversial issues on social media because their voice helps consumers engage more in such conversations.

This paper offers a theoretical framework to understand consumer reactions to CEO activism.

High levels of child obesity alongside rising stunting and the absence of a coherent food policy have deemed UK’s food system to be broken. The National Food Strategy (NFS) was debated intensely in media, with discussions on how and who should fix the food system.

Using a mixed methods approach, the authors conduct framing analysis on traditional media and sentiment analysis of twitter reactions to the NFS to identify frames used to shape food system policy interventions.

The study finds evidence that the media coverage of the NFS often utilised the tropes of “culture wars” shaping the debate of who is responsible to fix the food system – the government, the public or the industry. NFS recommendations were portrayed as issues of free choice to shift the debate away from government action correcting for market failure. In contrast, the industry was showcased as equipped to intervene on its own accord. Dietary recommendations made by the NFS were depicted as hurting the poor, painting a picture of helplessness and loss of control, while their voices were omitted and not represented in traditional media.

British media’s alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system. Media firms should move beyond tropes of culture wars to discuss interventions that reform the structural causes of the UK’s broken food systems.

As traditional media coverage struggles to capture the diversity of public perception; the authors supplement framing analysis with sentiment analysis of Twitter data. To the best of our knowledge, no such media (and social media) analysis of the NFS has been conducted. The paper is also original as it extends our understanding of how media alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system.

Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic, governments require sufficient crisis communication skills to engage citizens in taking appropriate action effectively. This study aims to examine how the National Health Commission of China (NHCC) has used TikTok, the leading short video–based platform, to facilitate public engagement during COVID-19.

Building upon dual process theories, this study integrates the activation of information exposure, prosocial interaction theory and social sharing of emotion theory to explore how public engagement is related to message sensation value (MSV), media character, content theme and emotional valence. A total of 354 TikTok videos posted by NHCC were collected during the pandemic to explore the determinants of public engagement in crises.

The findings demonstrate that MSV negatively predicts public engagement with government TikTok, but that instructional information increases engagement. The presence of celebrities and health-care professionals negatively affects public engagement with government TikTok accounts. In addition, emotional valence serves a moderating role between MSV, media characters and public engagement.

Government agencies must be fully aware of the different combinations of MSV and emotion use in the video title when releasing crisis-related videos. Government agencies can also leverage media characters – health professionals in particular – to enhance public engagement. Government agencies are encouraged to solicit public demand for the specific content of instructing information through data mining techniques.

This study aims to investigate the use of social networking sites (SNS) by researchers and their behaviours when conducting research-related activities by applying the unified theory of acceptance and use of technology (UTAUT) theory.

An online survey was distributed. This study’s design is derived from the UTAUT framework’s questionnaire items. The sample of this study comprised 216 respondents from 40 universities in the United Kingdom. Descriptive statistics were used to analyse the data.

Respondents revealed a positive relationship between the four constructs of the UTAUT framework (performance expectancy, effort expectancy, social influence and facilitating condition) associated with their intention to use SNS.

Most of the respondents were from the University of Strathclyde, so the authors cannot generalize the findings to other universities.

The findings will offer an extensive understanding of the value of SNSs, which will aid researchers to increase their visibility, and research activities online.

The results will provide an in-depth knowledge of the importance of SNSs, helping scholars to become more visible and engage in online research. A number of factors impacted how researchers behaved on SNSs and what they intended to use for research-related activity. School administrators, experts and other sponsors could take action to promote the use of SNSs in educational settings based on the findings. The study’s findings offer insightful knowledge to those who create SNS websites. By using this information, they will be able to improve these sites for research and study and gain a better understanding of the demands of SNS users.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.