Studying and Modeling the Connection between People's Preferences and Content Sharing

People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.Comment: CSCW 201

Exploring personality-targeted UI design in online social participation systems

We present a theoretical foundation and empirical findings demonstrating the effectiveness of personality-targeted design. Much like a medical treatment applied to a person based on his specific genetic profile, we argue that theory-driven, personality-targeted UI design can be more effective than design applied to the entire population. The empirical exploration focused on two settings, two populations and two personality traits: Study 1 shows that users' extroversion level moderates the relationship between the UI cue of audience size and users' contribution. Study 2 demonstrates that the effectiveness of social anchors in encouraging online contributions depends on users' level of emotional stability. Taken together, the findings demonstrate the potential and robustness of the interactionist approach to UI design. The findings contribute to the HCI community, and in particular to designers of social systems, by providing guidelines to targeted design that can increase online participation. Copyright © 2013 ACM

Modelling the influence of personality and culture on affect and enjoyment in multimedia

Affect is evoked through an intricate relationship between the characteristics of stimuli, individuals, and systems of perception. While affect is widely researched, few studies consider the combination of multimedia system characteristics and human factors together. As such, this paper explores the influence of personality (Five-Factor Model) and cultural traits (Hofstede Model) on the intensity of multimedia-evoked positive and negative affects (emotions). A set of 144 video sequences (from 12 short movie clips) were evaluated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three multilevel regression models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture; and an optimistic model in which each participant is modelled. An analysis shows that personal and cultural traits represent 5.6% of the variance in positive affect and 13.6% of the variance in negative affect. In addition, the affect-enjoyment correlation varied across the clips. This suggests that personality and culture play a key role in predicting the intensity of negative affect and whether or not it is enjoyed, but a more sophisticated set of predictors is needed to model positive affect with the same efficacy

Cross-Cultural Differences in the Perception of Group Entitativity and Autonomy

This research examined cross-cultural differences in group perceptions. Specifically, it examined the relative importance of the properties underlying perceived entitativity and the influence of entitativity on group autonomy beliefs among American and Japanese college students. Group properties were divided into two categories: essence properties and dynamic properties. Essence properties included similarities in group members’ physical characteristics, background, and personality traits. Dynamic properties included commonality in goals, outcomes, and cooperation among members. It was found that both American and Japanese people’s perceptions of entitativity were higher when essence and dynamic properties were high. However, essence properties were more strongly related to entitativity in the U.S. than in Japan, whereas dynamic properties were equally related. It was also found that the relationship between perceived group entitativity and perceived group autonomy depended on culture. Group autonomy beliefs were stronger and more strongly related to entitativity in the U.S. than in Japan

Dynamic Poisson Factorization

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.Comment: RecSys 201

A Survey on Prediction of Movie’s Box Office Collection Using Social Media

Predicting the box office profits of a movie prior to its world wide release are a significant but also an exigent problem that needs a advanced of Intelligence. Currently, social media has given away its diagnostic strength in a variety of fields, which encourages us to develop social media substance to predict box office profits. The collection of movies in provisions of profit relies on so many features for instance its making studio, type, screenplay superiority, pre release endorsement etc, each of which are usually utilized to approximation their probable achievement at the box office. Nevertheless, the “Wisdom of Crowd” and social media have been accredited as a powerful indication in appreciative customer activities to media. In this survey, we converse the influence of socially created Meta data derived from the social multimedia websites and review the effect of social media on box office collection and success of movies. This survey paper is written for (social networking) investigators who looking for to evaluate prediction of movies using social media. It gives a complete study of social media analytics for social networking, wikis, actually easy syndication feeds, blogs, newsgroups, chat and news feeds etc. Keywords: Social Networking, Social media. Movie’s Box Office, Prediction, Profitability, Sentiment analysi