What kind of content are you prone to tweet?: multi-topic preference model for tweeters

Comunicació presentada a: BIAS 2020: Bias and Social Aspects in Search and Recommendation celebrat el 14 d'abril de 2020 a Lisboa, Portugal.According to tastes, a person could show preference for a given category of content to a greater or lesser extent. However, quantifying people’s amount of interest in a certain topic is a challenging task, especially considering the massive digital information they are exposed to. For example, in the context of Twitter, aligned with his/her preferences a user may tweet and retweet more about technology than sports and do not share any music-related content. The problem we address in this paper is the identification of users’ implicit topic preferences by analyzing the content categories they tend to post on Twitter. Our proposal is significant given that modeling their multi-topic profile may be useful to find patterns or association between preferences for categories, discover trending topics and cluster similar users to generate better group recommendations of content. In the present work, we propose a method based on the Mixed Gaussian Model to extract the multidimensional preference representation for 399 Ecuadorian tweeters concerning twenty-two different topics (or dimensions) which became known by manually categorizing 68.186 tweets. Our experiment findings indicate that the proposed approach is effective at detecting the topic interests of users

Cascades on online social networks: a chronological account

Online social network platforms have served as a substantial venue for research, offering a plethora of data that can be analysed to cultivate insights about the way humans behave and interact within the virtual borders of these platforms. In addition to generating content, these platforms provide the means to spread content via built-in functionalities. The traces of the spreading content and the individuals’ incentives behind such behaviour are all parts of a phenomenon known as information diffusion. This phenomenon has been extensively studied in the literature from different perspectives, one of which is cascades: the traces of the spreading content. These traces form structures that link users to each other, where these links represent the direction of information flow between the users. In fact, cascades have served as an artefact to study the information diffusion processes on online social networks. In this paper, we present a survey of cascades; we consider their definitions and significance. We then look into their topology and what information is used to construct them and how the type of content and the platform can consequently affect cascades’ networks. Additionally, we present a survey of the structural and temporal features of cascades; we categorise them, define them and explain their significance, as these features serve as quantifiers to understand and overcome the complex nature of cascades

Positive or negative spirals of online behavior? Exploring reciprocal associations between being the actor and the recipient of prosocial and antisocial behavior online

Abstract: Bidirectional associations between being cyberbullied and cyberbullying others have been suggested, as well as bidirectional patterns of online prosocial behavior (reciprocity). However, so far, these relations have been studied as population-level associations, and it is not clear whether they also reflect within-person behavioral patterns. Therefore, this study aimed to disentangle between-person and within-person processes in online antisocial (cyberbullying) and prosocial behavior over time. Random intercept cross-lagged panel models were used to examine long-term within-person patterns of involvement in cyberbullying and online prosocial behavior. The findings showed no within-person effects between cyberbullying victimization and perpetration over time. In contrast, results did reveal significant within-person autoregressive effects of performing and receiving online prosocial behavior over time, and within-person cross-lagged effects between receiving online prosocial behavior and acting prosocially later on. These results indicate long-term positive, reinforcing spirals of prosocial exchanges, but no long-term negative spirals of cyberbullying perpetration and victimization