9,111 research outputs found

    Everyday the Same Picture: Popularity and Content Diversity

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    Facebook is flooded by diverse and heterogeneous content, from kittens up to music and news, passing through satirical and funny stories. Each piece of that corpus reflects the heterogeneity of the underlying social background. In the Italian Facebook we have found an interesting case: a page having more than 40K40K followers that every day posts the same picture of a popular Italian singer. In this work, we use such a page as a control to study and model the relationship between content heterogeneity on popularity. In particular, we use that page for a comparative analysis of information consumption patterns with respect to pages posting science and conspiracy news. In total, we analyze about 2M2M likes and 190K190K comments, made by approximately 340K340K and 65K65K users, respectively. We conclude the paper by introducing a model mimicking users selection preferences accounting for the heterogeneity of contents

    Quantifying the Effect of Sentiment on Information Diffusion in Social Media

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    Social media have become the main vehicle of information production and consumption online. Millions of users every day log on their Facebook or Twitter accounts to get updates and news, read about their topics of interest, and become exposed to new opportunities and interactions. Although recent studies suggest that the contents users produce will affect the emotions of their readers, we still lack a rigorous understanding of the role and effects of contents sentiment on the dynamics of information diffusion. This work aims at quantifying the effect of sentiment on information diffusion, to understand: (i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); (ii) what kind of emotions are more typical of popular conversations on social media; and, (iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics. Our findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-called positive bias. As for the entire conversations, we highlight how different temporal dynamics exhibit different sentiment patterns: for example, positive sentiment builds up for highly-anticipated events, while unexpected events are mainly characterized by negative sentiment. Our contribution is a milestone to understand how the emotions expressed in short texts affect their spreading in online social ecosystems, and may help to craft effective policies and strategies for content generation and diffusion.Comment: 10 pages, 5 figure

    How are you doing? : emotions and personality in Facebook

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    User generated content on social media sites is a rich source of information about latent variables of their users. Proper mining of this content provides a shortcut to emotion and personality detection of users without filling out questionnaires. This in turn increases the application potential of personalized services that rely on the knowledge of such latent variables. In this paper we contribute to this emerging domain by studying the relation between emotions expressed in approximately 1 million Facebook (FB) status updates and the users' age, gender and personality. Additionally, we investigate the relations between emotion expression and the time when the status updates were posted. In particular, we find that female users are more emotional in their status posts than male users. In addition, we find a relation between age and sharing of emotions. Older FB users share their feelings more often than young users. In terms of seasons, people post about emotions less frequently in summer. On the other hand, December is a time when people are more likely to share their positive feelings with their friends. We also examine the relation between users' personality and their posts. We find that users who have an open personality express their emotions more frequently, while neurotic users are more reserved to share their feelings

    Predicting Community Evolution in Social Networks

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    Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page

    Is Twitter a Public Sphere for Online Conflicts? A Cross-Ideological and Cross-Hierarchical Look

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    The rise in popularity of Twitter has led to a debate on its impact on public opinions. The optimists foresee an increase in online participation and democratization due to social media's personal and interactive nature. Cyber-pessimists, on the other hand, explain how social media can lead to selective exposure and can be used as a disguise for those in power to disseminate biased information. To investigate this debate empirically, we evaluate Twitter as a public sphere using four metrics: equality, diversity, reciprocity and quality. Using these measurements, we analyze the communication patterns between individuals of different hierarchical levels and ideologies. We do this within the context of three diverse conflicts: Israel-Palestine, US Democrats-Republicans, and FC Barcelona-Real Madrid. In all cases, we collect data around a central pair of Twitter accounts representing the two main parties. Our results show in a quantitative manner that Twitter is not an ideal public sphere for democratic conversations and that hierarchical effects are part of the reason why it is not.Comment: To appear in the 6th International Conference on Social Informatics (SocInfo 2014), Barcelon

    The Italian version of the Thinking About Life Experiences Questionnaire and its relationship with gender, age, and life events on Facebook

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    The present study provided a cross-cultural validation of the Thinking About Life Experiences Scale-revised (TALE-R) in an Italian sample of Facebook users (n = 492; female = 378; male = 114; mean age 26.1) to test for replication and universality of the TALE-R three-factor model. Furthermore, it explored the interrelations among gender, age, the scores at the TALE-R and the frequency of posting textual/visual information about individuals' life events on Facebook. Results at exploratory and confirmatory factor analysis gave empirical support to both of a tripartite model for the functions of autobiographical memory (i.e., directive-behavior, social-bonding, and self-continuity) and measurement invariance of this three-factor model across gender and age. Further results at linear correlation and regression analyses showed that directive-behavior and self-continuity functions of autobiographical memory are significantly related to the ways people use Facebook for personal documentation. Age differences more than gender influence this association. Discussion and conclusion reported both theoretical and empirical implications of the findings of the study
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