91 research outputs found
A Dynamical Model of Twitter Activity Profiles
The advent of the era of Big Data has allowed many researchers to dig into
various socio-technical systems, including social media platforms. In
particular, these systems have provided them with certain verifiable means to
look into certain aspects of human behavior. In this work, we are specifically
interested in the behavior of individuals on social media platforms---how they
handle the information they get, and how they share it. We look into Twitter to
understand the dynamics behind the users' posting activities---tweets and
retweets---zooming in on topics that peaked in popularity. Three mechanisms are
considered: endogenous stimuli, exogenous stimuli, and a mechanism that
dictates the decay of interest of the population in a topic. We propose a model
involving two parameters and describing the tweeting
behaviour of users, which allow us to reconstruct the findings of Lehmann et
al. (2012) on the temporal profiles of popular Twitter hashtags. With this
model, we are able to accurately reproduce the temporal profile of user
engagements on Twitter. Furthermore, we introduce an alternative in classifying
the collective activities on the socio-technical system based on the model.Comment: 10 pages, 5 figure
The Ebb and Flow of Controversial Debates on Social Media
We explore how the polarization around controversial topics evolves on
Twitter - over a long period of time (2011 to 2016), and also as a response to
major external events that lead to increased related activity. We find that
increased activity is typically associated with increased polarization;
however, we find no consistent long-term trend in polarization over time among
the topics we study.Comment: Accepted as a short paper at ICWSM 2017. Please cite the ICWSM
version and not the ArXiv versio
#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter
Demographics, in particular, gender, age, and race, are a key predictor of
human behavior. Despite the significant effect that demographics plays, most
scientific studies using online social media do not consider this factor,
mainly due to the lack of such information. In this work, we use
state-of-the-art face analysis software to infer gender, age, and race from
profile images of 350K Twitter users from New York. For the period from
November 1, 2014 to October 31, 2015, we study which hashtags are used by
different demographic groups. Though we find considerable overlap for the most
popular hashtags, there are also many group-specific hashtags.Comment: This is a preprint of an article appearing at ICWSM 201
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
Modeling trend progression through an extension of the Polya Urn Process
Knowing how and when trends are formed is a frequently visited research goal.
In our work, we focus on the progression of trends through (social) networks.
We use a random graph (RG) model to mimic the progression of a trend through
the network. The context of the trend is not included in our model. We show
that every state of the RG model maps to a state of the Polya process. We find
that the limit of the component size distribution of the RG model shows
power-law behaviour. These results are also supported by simulations.Comment: 11 pages, 2 figures, NetSci-X Conference, Wroclaw, Poland, 11-13
January 2016. arXiv admin note: text overlap with arXiv:1502.0016
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
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