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The purpose of this paper is to provide a method to examine the differences in behaviour during a post‐quake period.

Fieldwork and questionnaires were used to collect the households’ members’ movement behaviours after the 2004 Chuetsu Earthquake. In total, three study areas were selected in Kawaguchi town (Niigata Prefecture) in order to enhance how the visualisation process can provide support in better understanding the behaviour during evacuation and recovery process. For this purpose the Space‐Time‐Cube (STC) was used to represent and analyse residents’ movement paths over time.

Differences appear in the spatio‐temporal paths of the three study areas, implying a connection between the geographical location and movement patterns. The city centre shows disorganized Spatio‐Temporal‐Patterns (STPs) during the first week of the recovery process, eventually becoming organized after the rescuers’ arrival. Moving towards the isolated areas of the town, a progressive STP organisation can be observed, explaining the faster response after the seismic event.

Spatio‐temporal data are difficult and costly to collect, especially if a long period of time passes between the seismic event and the survey.

The STC can be used as tool to enhance the disaster management techniques and provide support in crisis situations.

The paper provides a practical approach to investigate the reactions after a seismic event and can be used in larger study areas to develop better strategies in disaster management.

Scholarship on the state control of social movements has predominately focused on overt repression, resulting in comparatively less attention to more covert forms of control. Researchers have suggested that government surveillance of social movement organizations (SMOs) has become increasingly widespread and routinized in the post-September 11, 2001 era, but this hypothesis has remained untested. Since contemporary surveillance is grounded in a logic of information gathering that has diffused across law enforcement agencies since the September 11 attacks, government actors now cast a wide net and monitor a large variety of groups. This study shows that a result, traditional factors predicting surveillance, such as contentious behavior, have less explanatory power. Using a database of 409 SMOs active in Philadelphia between January 1996 and October 2009, the research asked who and why particular groups are monitored by the Pennsylvania Office of Homeland Security (PA-OHS) between November 2009 and September 2010. Bayesian logistic regression analysis is used to examine the variables predicting surveillance. Findings show that 23% of the SMOs in the sample were targets of surveillance. Organizational ideology was the strongest predictor and there was little evidence that history of contentious protests or previous conflict with the police influenced coming under surveillance. However, groups with less visibility in traditional media sources were more likely to be monitored.

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.

A proposition offered in this manuscript is that activist musicians use their musical competencies to enhance their social change strategies within the local community. However, it is unclear what strategies are being utilized by local activist musicians in order to reach collective action and achieve social and political change. A self-developed framework, the Framework for Activist Musicians (FAM), portrays how an activist musician utilizes their social experiences, behaviors, and influence to enact social change. The framework delineates how a musician utilizes their music-making involvement and status to enhance their charisma and authenticity as an activist to establish social change. Additionally, the framework outlines the unique qualities of a musician and activist which make them well-prepared to be an influential community leader.

Exponential growth in online video content makes viewing choice and video promotion increasingly challenging. While explicit recommendation systems have value, they inherently distract the user from normal behaviour and are open to numerous biases. To enhance user interest evaluation accuracy, the purpose of this paper is to comprehensively examine the relationship between implicit feedback and online video content, and reviews gender differentials in the interest indicated by a comprehensive set of viewer responses.

This paper includes 200 useable observations based on an experiment of user interaction with the Youku platform (one of the largest video-hosting websites in China). Logistic regression was employed for its simple interpretation to test the proposed hypotheses.

The findings demonstrate gender differentials in cursor movement behaviour, explainable via well-studied splits in personality, biological factors, primitive behaviour and emotion management. This work offers a solution to the sparsity of work on implicit feedback, contributing to the literature that combines explicit and implicit feedback.

This study offers a launch point for further work on human–computer interaction, and highlights the importance of looking beyond individual metrics to embrace wider human traits in video site design and implementation.

This paper links implicit feedback to online video content for the first time, and demonstrates its value as an interest capturing tool. By reviewing gender differentials in the interest indicated by a comprehensive set of viewer responses, this paper indicates how user characteristics remain critical. Consequently, this work signposts highly fruitful directions for both practitioners and researchers.

Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements.

First, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product.

The results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods.

User mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction.

Mouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

This study aims to understand how college students' personal and health-related characteristics are related to their reading behaviors and cognitive outcomes of Facebook health information through eye tracking data and cognitive outcomes.

This study analyzed users' gaze movement data and results of recall and recognition tests to investigate users' reading patterns and their consequences with cognitive outcomes. The gaze movements are analyzed with eye tracking data including the average fixation count and time to first fixation.

The results of reading patterns show that Texts and Images are highly viewed and viewed immediately by participants when the posts were presented. There was no clear pattern with fixation counts to determine cognitive outcomes. However, the findings of study suggest that there is a clear pattern of reading Facebook posts with areas of interest (AOIs). Among five AOIs observed, participants viewed Images first and then Texts when a Facebook post is presented. On the other hand, they read Texts more carefully than Images. The findings of this study suggest that while images contribute to gaining users' attention, a clear and precise message needs to be delivered in text message to ensure readers' correct understanding and application of health information.

The user-centered evidence on reading behaviors and cognitive outcomes will make contributions to how health professionals and health organizations can make optimal use of Facebook for effective health information communication.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0177

All societies carry out sorting and classificatory actions, the way they view deviance changes over time for a variety of reasons that are sometimes unrelated to the behavior or its consequences (Moynihan, 1993). Also, some behaviors that were considered to be illnesses or crimes at one time have been redefined in ways that remove them from the medical, psychological, or legal professions' guidelines for interpreting them as deviant behaviors. Homosexuality is one example of such a reclassification (Bowker & Star, 1999).

Detecting emotion on user experience of web applications and browsing is important in many ways. Web designers and developers find such approach quite useful in enhancing navigational features of webpages, and biomedical personnel regularly use computer simulations to monitor and control the behaviour of patients. On the other hand, law enforcement agents rely on human physiological functions to determine the likelihood of falsehood in interrogations. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users' emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors.

The authors propose a novel method-a window dynamic control system that addresses the foregoing issues. Primary data were obtained from laboratory experiments during which forty-four volunteers had their synchronised physiological readings, skin conductance response (SCR), skin temperature (ST), eye movement behaviour and users’ activity attributes taken using biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronised physiological readings and uses them for two purposes. For both detection of optimal emotional responses and users' stress levels. The method's novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage.

Results show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods.

The paper is limited to using a window control system to detect emotions on webpages, while integrated to biosensors and eye-tracker.

The originality of the proposed model is its resistance to overfitting and its ability to automatically assess human emotion (stress levels) while dealing with specific web contents. The latter is particularly important in that it can be used to predict which contents of webpages cause stress-induced emotions to users when involved in online activities.