Understanding the Influence of Temporal Focus on Users’ Self-Disclosure on Social Networking Sites

Self disclosure decision making on social networking sites (SNSs) can be considered an intertemporal choice between gaining benefits at the present and experiencing privacy harm in the future. Prior research shows that people tend to overemphasize the immediate benefits while discounting the delayed risks, but it remains unclear how and why different SNS users may subjectively discount the long term risks against the short-term benefits. This paper considers heterogeneity in users’ self disclosure decisions by focusing on the effects of temporal focus (i.e., the degree to which people think about the past, present, and future) on users’ self disclosure willingness. Using online experiments, this study tests the effectiveness of different interventions that manipulate people’s temporal focus in influencing SNS self disclosure willingness. The findings of this study provide practical implications for the design of SNS platforms and development of data policies

Unveiling Real-Life Effects of Online Photo Sharing

Social networks give free access to their services in exchange for the right to exploit their users' data. Data sharing is done in an initial context which is chosen by the users. However, data are used by social networks and third parties in different contexts which are often not transparent. In order to unveil such usages, we propose an approach which focuses on the effects of data sharing in impactful real-life situations. Focus is put on visual content because of its strong influence in shaping online user profiles. The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation. These components are combined in LERVUP, a method which learns to rate visual user profiles in each situation. LERVUP exploits a new image descriptor which aggregates object ratings and object detections at user level and an attention mechanism which boosts highly-rated objects to prevent them from being overwhelmed by low-rated ones. Performance is evaluated per situation by measuring the correlation between the automatic ranking of profile ratings and a manual ground truth. Results indicate that LERVUP is effective since a strong correlation of the two rankings is obtained. A practical implementation of the approach in a mobile app which raises user awareness about shared data usage is also discussed

Cognitive Biases, Dark Patterns, and the ‘Privacy Paradox’

Scholars and commentators often argue that individuals do not care about their privacy, and that users routinely trade privacy for convenience. This ignores the cognitive biases and design tactics platforms use to manipulate users into disclosing information. This essay highlights some of those cognitive biases – from hyperbolic discounting to the problem of overchoice – and discusses the ways in which platform design can manipulate disclosure. It then explains how current law allows this manipulative and anti-consumer behavior to continue and proposes a new approach to reign in the phenomenon

Muslim Diaspora in the West and International HRM

Interest in Islam and how Muslims organise themselves within the so-called Western world has largely stemmed from the flow of Muslim immigration since the 1960s and the 1970s (Loobuyck, Debeer, & Meier, 2013). Many of these immigrants have come to these new lands in the hope of making a better life for themselves economically, or to escape the political or religious pressures of their homeland (Lebl, 2014). Initially, deeming the influx of these foreigners to be largely irrelevant, there was little interest in their presence by the different governments across many jurisdictions. Typically, scant interest was shown towards entering into dialogue with the Muslim immigrant community. Indeed, until the 1990s, it was not uncommon for Islam to be perceived as a strange, foreign religion that was best managed through outsourcing to respective consulates (Loobuyck et al., 2013). Yet, migration and work-based mobility has a significant influence on the world of work and societies in which organisations are embedded. Many individuals migrate for better employment perspectives, as well as due to chain migration, betterment in the quality of life and based on fleeing famine, war and terror zones globally (Sharma & Reimer-Kirkham, 2015; Valiūnienė, 2016). Migration could involve upward as well as downward mobility/ wages, depending on the country and organisation. For example, minimum wages differ from € 184 in Bulgaria up to € 1923 in Luxembourg (Valiūnienė, 2016). Migration also contributes to the lived religion of diasporic communities as they navigate their faith at work (Sharma & Reimer-Kirkha

Vers une plus grande transparence du Web

International audienceDe plus en plus les géants du Web (Amazon, Google et Twitter en tête) recourent a la manne des « Big data » : ils collectent une myriade de données qu'ils exploitent pour leurs algorithmes de recommandation personnalisée et leurs campagnes publicitaires. Pareilles méthodes peuvent considérablement améliorer les services rendus a leurs utilisateurs, mais leur opacité fait débat. En effet, il n'existe pas a ce jour d'outil suffisamment robuste qui puisse tracer sur le Web l'usage des données et des informations sur un utilisateur par des services en ligne. Motivés par ce manque de transparence, nous avons développé un prototype du nom d'XRay, et qui peut prédire quelle donnée parmi toutes celles présentes dans un compte utilisateur est responsable de la réception d'une publicité. Dans cet article, nous présentons son principe ainsi que les résultats de nos premières expérimentations. Nous introduisons dans le même temps le tout premier modèle théorique pour le problème de la transparence du Web, et nous interprétons les performances d'Xray a la lumière de nos résultats obtenus dans ce modèle. En particulier, nous démontrons qu'un nombre θ(log N) de comptes utilisateurs auxiliaires, remplis selon un procédé aléatoire , suffisent a déterminer quelle donnée parmi les N en présence a causé la réception d'une publicité. Nous aborderons brièvement les extensions possibles, et quelques problèmes ouverts

Racial Discrimination in Social Media Customer Service: Evidence from a Popular Microblogging Platform

The concept of racial inequality has existed from the early days of service provision, with evidence dating back to ancient civilizations. While the emergence of the Internet and social media has drastically transformed almost every aspect of everyday life, including the intrinsic values of social relationships, the impact of racial disparities on receiving services on online platforms is not so evident. Although many consumer brands provide customer service on social media today, little is known regarding the prevalence and magnitude of racial discrimination in the context of social media customer service. Thus, in this study, we examine the existence and the extent of racial discrimination against African-Americans in social media customer service. We analyzed all complaints to seven major U.S. airlines on Twitter for a period of nine months. Interestingly, our empirical analysis finds that African-American customers are less likely to receive brand responses to their complaints on social media. To the best of our knowledge, this is the first study to empirically analyze the racial discrimination phenomenon in the context of social media customer service

How Unbecoming of You: Gender Biases in Perceptions of Ridesharing Performance

It has been suggested that the gig-economy’s elimination of traditional arm’s-length transactions may introduce bias into perceptions of quality. In this work, we build upon research that has identified biases based on ascriptive characteristics in rating systems, and examine gender biases in ridesharing platforms. In doing so, we extend research to consider not simply willingness to transact, but post transaction perceptions of quality. We also examine which types of tasks may yield more biased ratings for female drivers. We find no differences in ratings across gender in the presence of a high quality experience. However, when there is a lower quality experience, penalties for women accrue faster, notably when poorly performed tasks are perceived to be highly gendered

Discrimination in the Age of Social Media: The New Dangers of Cat's Paw Liability

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