Publications by authors named "Denis Helic"

Wikipedia is an important source of general knowledge covering a wide range of topics. Moreover, for many people around the world, it also serves as an essential news source for major events such as elections or disasters. Although Wikipedia covers many such events, some events are underrepresented and lack attention, despite their newsworthiness predicted from news value theory.

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In recent years a number of studies have used objective gambling data from online gambling operators to study gambling behavior. A few of these studies have compared gamblers' actual gambling behavior (using account-based tracking data) with their subjective gambling behavior (using responses from survey data). The present study extended previous studies by comparing self-reported money deposited with the actual amount of money deposited.

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Online gambling is a socially acceptable means of entertainment, but it can also have a negative impact on many areas of life and lead to problem gambling for a minority of individuals. In recent years, gambling operators have increasingly implemented responsible gambling tools to help at-risk gamblers control and limit their gambling. One such tool is voluntary self-exclusion (VSE), where gamblers can exclude themselves from the gambling platform for a self-selected period of time.

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The prevention of problematic online gambling behavior is a topic of major interest for regulators, the gambling industry, and researchers. Many gambling operators approach this issue by using responsible gambling tools. Among such tools, mandatory play breaks are used to interrupt long online gambling sessions, providing "cooling off" periods for players to take a reflective "time out".

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Article Synopsis
  • The study addresses the issue of problematic gambling behavior and the role of self-exclusion tools offered by operators to help players manage their gambling.
  • It analyzes player tracking data from three online gambling platforms across six countries, focusing on what factors lead players to choose self-exclusion.
  • Findings reveal that behavioral features like previous limit changes, deposit habits, and game variety are significant predictors of future self-exclusion, while monetary factors like amount wagered did not significantly influence these odds.
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Article Synopsis
  • Wikipedia is a global encyclopedia created by volunteers, and its contributions were questioned during the COVID-19 pandemic.
  • During the pandemic, volunteers not only maintained their contributions but also significantly increased them, especially in the English Wikipedia.
  • This analysis provides insights into how online communities, like Wikipedia, adapt and thrive under stressful conditions, highlighting the resilience of volunteer efforts.
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Background: In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics.

Methods: For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status.

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Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far.

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In this work we study how people navigate the information network of Wikipedia and investigate (i) free-form navigation by studying all clicks within the English Wikipedia over an entire month and (ii) goal-directed Wikipedia navigation by analyzing wikigames, where users are challenged to retrieve articles by following links. To study how the organization of Wikipedia articles in terms of layout and links affects navigation behavior, we first investigate the characteristics of the structural organization and of hyperlinks in Wikipedia and then evaluate link selection models based on article structure and other potential influences in navigation, such as the generality of an article's topic. In free-form Wikipedia navigation, covering all Wikipedia usage scenarios, we find that click choices can be best modeled by a bias towards article structure, such as a tendency to click links located in the lead section.

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In this paper, we analyze the influence of social status on opinion dynamics and consensus building in collaboration networks. To that end, we simulate the diffusion of opinions in empirical networks and take into account both the network structure and the individual differences of people reflected through their social status. For our simulations, we adapt a well-known Naming Game model and extend it with the to account for the social status of individuals participating in a meeting.

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The need to examine the behavior of different user groups is a fundamental requirement when building information systems. In this paper, we present Ontology-based Decentralized Search (OBDS), a novel method to model the navigation behavior of users equipped with different types of background knowledge. Ontology-based Decentralized Search combines decentralized search, an established method for navigation in social networks, and ontologies to model navigation behavior in information networks.

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One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others.

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