5,788 research outputs found
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
Quantifying echo chamber effects in information spreading over political communication networks
Echo chambers in online social networks, in which users prefer to interact
only with ideologically-aligned peers, are believed to facilitate
misinformation spreading and contribute to radicalize political discourse. In
this paper, we gauge the effects of echo chambers in information spreading
phenomena over political communication networks. Mining 12 million Twitter
messages, we reconstruct a network in which users interchange opinions related
to the impeachment of the former Brazilian President Dilma Rousseff. We define
a continuous {political position} parameter, independent of the network's
structure, that allows to quantify the presence of echo chambers in the
strongly connected component of the network, reflected in two well-separated
communities of similar sizes with opposite views of the impeachment process. By
means of simple spreading models, we show that the capability of users in
propagating the content they produce, measured by the associated spreadability,
strongly depends on their attitude. Users expressing pro-impeachment sentiments
are capable to transmit information, on average, to a larger audience than
users expressing anti-impeachment sentiments. Furthermore, the users'
spreadability is correlated to the diversity, in terms of political position,
of the audience reached. Our method can be exploited to identify the presence
of echo chambers and their effects across different contexts and shed light
upon the mechanisms allowing to break echo chambers.Comment: 9 pages, 4 figures. Supplementary Information available as ancillary
fil
What increases (social) media attention: Research impact, author prominence or title attractiveness?
Do only major scientific breakthroughs hit the news and social media, or does
a 'catchy' title help to attract public attention? How strong is the connection
between the importance of a scientific paper and the (social) media attention
it receives? In this study we investigate these questions by analysing the
relationship between the observed attention and certain characteristics of
scientific papers from two major multidisciplinary journals: Nature
Communication (NC) and Proceedings of the National Academy of Sciences (PNAS).
We describe papers by features based on the linguistic properties of their
titles and centrality measures of their authors in their co-authorship network.
We identify linguistic features and collaboration patterns that might be
indicators for future attention, and are characteristic to different journals,
research disciplines, and media sources.Comment: Paper presented at 23rd International Conference on Science and
Technology Indicators (STI 2018) in Leiden, The Netherland
Markets, herding and response to external information
We focus on the influence of external sources of information upon financial
markets. In particular, we develop a stochastic agent-based market model
characterized by a certain herding behavior as well as allowing traders to be
influenced by an external dynamic signal of information. This signal can be
interpreted as a time-varying advertising, public perception or rumor, in favor
or against one of two possible trading behaviors, thus breaking the symmetry of
the system and acting as a continuously varying exogenous shock. As an
illustration, we use a well-known German Indicator of Economic Sentiment as
information input and compare our results with Germany's leading stock market
index, the DAX, in order to calibrate some of the model parameters. We study
the conditions for the ensemble of agents to more accurately follow the
information input signal. The response of the system to the external
information is maximal for an intermediate range of values of a market
parameter, suggesting the existence of three different market regimes:
amplification, precise assimilation and undervaluation of incoming information.Comment: 30 pages, 8 figures. Thoroughly revised and updated version of
arXiv:1302.647
Linguistic Markers of Influence in Informal Interactions
There has been a long standing interest in understanding `Social Influence'
both in Social Sciences and in Computational Linguistics. In this paper, we
present a novel approach to study and measure interpersonal influence in daily
interactions. Motivated by the basic principles of influence, we attempt to
identify indicative linguistic features of the posts in an online knitting
community. We present the scheme used to operationalize and label the posts
with indicator features. Experiments with the identified features show an
improvement in the classification accuracy of influence by 3.15%. Our results
illustrate the important correlation between the characteristics of the
language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for
Computational Linguistics) 201
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