9,111 research outputs found
Everyday the Same Picture: Popularity and Content Diversity
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
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 likes and comments, made by approximately and
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
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
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
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
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
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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