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The purpose of this paper is to explore the spillover effects of offensive commenting in online community from the lens of emotional and behavioral contagion. Specifically, it examines the contagion of swearing – a linguistic mannerism that conveys high-arousal emotion – based upon two mechanisms of contagion: mimicry and social interaction effect.

The study performs a series of mixed-effect logistic regressions to investigate the contagious potential of offensive comments collected from YouTube in response to Donald Trump’s 2016 presidential campaign videos posted between January and April 2016.

The study examines non-random incidences of two types of swearing online: public and interpersonal. Findings suggest that a first-level (a.k.a. parent) comment’s public swearing tends to trigger chains of interpersonal swearing in the second-level (a.k.a. child) comments. Meanwhile, among the child-comments, a sequentially preceding comment’s swearing is contagious to the following comment only across the same swearing type. Based on the findings, the study concludes that offensive comments are contagious and have impact on shaping the community-wide linguistic norms of online user interactions.

The study discusses the ways in which an individual’s display of offensiveness may influence and shape discursive cultures on the internet. This study delves into the mechanisms of text-based contagion by differentiating between mimicry effect and social interaction effect. While online emotional contagion research to this date has focused on the difference between positive and negative valence, internet research that specifically looks at the contagious potential of offensive expressions remains sparse.

This chapter explores collective information processing among black-hat hackers during their crises events. The chapter presents a preliminary study on one of Tor-based darknet market forums, during the shutdowns of two cryptomarkets. Content and network analysis of forum conversations showed that black-hat users mostly engaged with rational information processing and were adept at reaching collective solutions by sharing security advices, new market information, and alternative routes for economic activities. At the same time, the study also found that anti-social and distrustful interactions were aggravated during the marketplace shutdowns. Communication network analysis showed that not all members were affected by the crisis events, alluding to a fragmented network structure of black-hat markets. The chapter concludes that, while darknet forums may constitute resilient, solution-oriented users, market crises potentially make the community vulnerable by engendering internal distrust.

In the age of DarkNetMarkets proliferation, combatting money laundering has become even more complicated. Constantly evolving technologies add a new layer of difficulty to already intricated schemes of hiding the cryptocurrency’s origin. Considering the latest development of cryptocurrency- and blockchain-related use cases, this study aims to scrutinize Italian and Russian antimoney laundering regulations to understand their preparedness for a new era of laundering possibilities.

One of the most recommended ways to buy and sell cryptocurrencies for illegal drug trade on DarkNet was discovered using machine learning, i.e. natural language processing and topic modeling. This study compares how current Italian and Russian laws address this technique.

Despite differences in cryptocurrency regulation, both the Italian Republic and the Russian Federation fall behind on preventing cryptolaundering.

The main contributions of this paper: consideration of noncustodial wallet projects and nonfungible token platforms through the lens of money laundering opportunities, comparison of Italian and Russian antimoney laundering regulations related to cryptocurrency, empirical analysis of the preferred method of trading/exchanging cryptocurrency for DarkNet illegal trade using machine learning techniques and the assessment of how Italian and Russian regulations address these money laundering methods.

This critical content analysis aims to examine the depiction of oppression in the 2022 Notable Social Studies Trade Books (K-2). From the framework of major types and levels of oppression, this paper sheds light on the rich affordances and problematic representations of oppression.

From the perspectives of an intersectional approach and the framework of oppression, the authors conducted a critical content analysis of the written texts, illustrations and peritexts of the notable books for young readers.

Among the 73 picturebooks, 46 (63%) include representations of oppression in the written texts and/or illustrations. Half of these books depict more than one type of oppression. The most frequently represented oppression is racism, followed by sexism. There are limited depictions of homophobia, transphobia, ableism, ageism, antisemitism and Islamophobia. Nine books (20%) only include the representation of oppression in the peritexts.

This study contributes to anti-oppressive education by offering a theoretical framework of oppression, which emphasizes the interlocking systems of oppression. This framework can help foster a holistic understanding of oppression and dismantle it in a holistic way.

The authors also offer suggestions to help educators curate picturebooks for anti-oppressive social studies education.

This study contributes to anti-oppressive education by offering a theoretical framework of oppression, which emphasizes the interlocking systems of oppression. This framework can help foster a holistic understanding of oppression and dismantle it in a holistic way. The authors also offer suggestions to help educators curate picturebooks for anti-oppressive social studies education.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

Given the important role of corporate social responsibility (CSR) to enhance company performance, the purpose of this paper is to fill the existing gaps in the hotel CSR literature via application of the loyalty formation mechanism and conceptualizations of different aspects of CSR initiatives.

The current study examines the effect of environmental and social CSR strategies on multidimensional brand loyalty of hotel customers (i.e. cognitive, affective and conative). This study uses the partial least squares–structural equation modeling to examine the effect of CSR types on multidimensional loyalty.

The results reveal that environmental and social CSR strategies have a significant positive effect on all three loyalty responses of hotel customers with a different level of power. Especially, environmental CSR is highly correlated with conative loyalty, while social CSR is highly correlated with cognitive and affective loyalty responses.

Limited studies have applied the multidimensional attitudinal loyalty in the CSR context. Thus, this study brings theoretical and practical implications. The findings of this study indicate that customers’ perception of hotel CSR could be directly incorporated into their patronized attitudes.

This study provides an empirical guideline for monitoring CSR initiatives from the customers’ perspectives.

This study sought to understand which food allergen labeling systems (non-directive, semi-directive, and directive) were attended to and preferred by 34 participants with food hypersensitivity and their perceived corporate social responsibility (CSR) and behavioral intention towards a restaurant that identifies food allergens on menus.

This study used an online survey with open-ended and ranking questions, combined with eye-tracking technology, to explore participants' visual attention and design preferences regarding four menus. This study utilized one-way repeated measures analysis of variance (RM-ANOVA) and heat maps to analyze participants' menu-reading behaviors. A content analysis of survey responses and a ranking analysis of menus were conducted to understand the reasons behind consumers' preferred menu designs.

The advisory statement was not much attended to. Participants identified food allergen information significantly quicker with the directive labeling system (icons) than the other two systems, implying they were eye-catching. Semi-directive labeling system (red text) has lower visit count and was more preferred than two other systems; each labeling system has its strengths and limitations. Participants viewed restaurants that disclosed food allergen information on menus as socially responsible, and they would revisit those restaurants in the future.

This study was one of the first to explore, through use of eye-tracking technology, which food allergen labeling systems were attended to by consumers with food hypersensitivity. The use of triangulation methods strengthened the credibility of the results. The study provided empirical data to restauranteurs in the US on the values of food allergen identification on restaurant menus, although it is voluntary.

The purpose of this study is to specifically look at the relationship between reliable network reliance and entrepreneurial performance.

To help achieve the study objective, 450 rural farmer-entrepreneurs from Jiangsu, Anhui, Guangxi and Zhejiang Provinces were randomly selected. The study concentrated mainly on entrepreneurs who have engaged in entrepreneurial activities the past five or more years.

This study’s findings show that network reliance has direct and indirect effects on entrepreneurial performance through external networking behaviour. The result further shows that external networking behaviour partially mediates the relationship between network reliance and entrepreneurial performance. On the moderation effect on entrepreneurial orientation (EO), the result reveals that it strengthens the relationship between external networking behaviour and entrepreneurial performance. The result shows that EO has a direct effect on entrepreneurial performance. The paper introduces behavioural component of network to entrepreneurial performance. The study concludes that external networking behaviour of entrepreneurs is key in entrepreneurship as it improves relationships among actors and thereby translating into an improved performance.

The paper brings to light the need to reconsider extension education by including elements of networking to enable rural entrepreneurs derive full benefits of their entrepreneurial ventures.