31,660 research outputs found
Dynamics of Information Diffusion and Social Sensing
Statistical inference using social sensors is an area that has witnessed
remarkable progress and is relevant in applications including localizing events
for targeted advertising, marketing, localization of natural disasters and
predicting sentiment of investors in financial markets. This chapter presents a
tutorial description of four important aspects of sensing-based information
diffusion in social networks from a communications/signal processing
perspective. First, diffusion models for information exchange in large scale
social networks together with social sensing via social media networks such as
Twitter is considered. Second, Bayesian social learning models and risk averse
social learning is considered with applications in finance and online
reputation systems. Third, the principle of revealed preferences arising in
micro-economics theory is used to parse datasets to determine if social sensors
are utility maximizers and then determine their utility functions. Finally, the
interaction of social sensors with YouTube channel owners is studied using time
series analysis methods. All four topics are explained in the context of actual
experimental datasets from health networks, social media and psychological
experiments. Also, algorithms are given that exploit the above models to infer
underlying events based on social sensing. The overview, insights, models and
algorithms presented in this chapter stem from recent developments in network
science, economics and signal processing. At a deeper level, this chapter
considers mean field dynamics of networks, risk averse Bayesian social learning
filtering and quickest change detection, data incest in decision making over a
directed acyclic graph of social sensors, inverse optimization problems for
utility function estimation (revealed preferences) and statistical modeling of
interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112
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
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
Bots increase exposure to negative and inflammatory content in online social systems
Societies are complex systems which tend to polarize into sub-groups of
individuals with dramatically opposite perspectives. This phenomenon is
reflected -- and often amplified -- in online social networks where, however,
humans are no more the only players, and co-exist alongside with social bots,
i.e., software-controlled accounts. Analyzing large-scale social data collected
during the Catalan referendum for independence on October 1, 2017, consisting
of nearly 4 millions Twitter posts generated by almost 1 million users, we
identify the two polarized groups of Independentists and Constitutionalists and
quantify the structural and emotional roles played by social bots. We show that
bots act from peripheral areas of the social system to target influential
humans of both groups, bombarding Independentists with violent contents,
increasing their exposure to negative and inflammatory narratives and
exacerbating social conflict online. Our findings stress the importance of
developing countermeasures to unmask these forms of automated social
manipulation.Comment: 8 pages, 5 figure
Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
Consuming news from social media is becoming increasingly popular. However,
social media also enables the widespread of fake news. Because of its
detrimental effects brought by social media, fake news detection has attracted
increasing attention. However, the performance of detecting fake news only from
news content is generally limited as fake news pieces are written to mimic true
news. In the real world, news pieces spread through propagation networks on
social media. The news propagation networks usually involve multi-levels. In
this paper, we study the challenging problem of investigating and exploiting
news hierarchical propagation network on social media for fake news detection.
In an attempt to understand the correlations between news propagation
networks and fake news, first, we build a hierarchical propagation network from
macro-level and micro-level of fake news and true news; second, we perform a
comparative analysis of the propagation network features of linguistic,
structural and temporal perspectives between fake and real news, which
demonstrates the potential of utilizing these features to detect fake news;
third, we show the effectiveness of these propagation network features for fake
news detection. We further validate the effectiveness of these features from
feature important analysis. Altogether, this work presents a data-driven view
of hierarchical propagation network and fake news and paves the way towards a
healthier online news ecosystem.Comment: 10 page
Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams
Online social media are complementing and in some cases replacing
person-to-person social interaction and redefining the diffusion of
information. In particular, microblogs have become crucial grounds on which
public relations, marketing, and political battles are fought. We introduce an
extensible framework that will enable the real-time analysis of meme diffusion
in social media by mining, visualizing, mapping, classifying, and modeling
massive streams of public microblogging events. We describe a Web service that
leverages this framework to track political memes in Twitter and help detect
astroturfing, smear campaigns, and other misinformation in the context of U.S.
political elections. We present some cases of abusive behaviors uncovered by
our service. Finally, we discuss promising preliminary results on the detection
of suspicious memes via supervised learning based on features extracted from
the topology of the diffusion networks, sentiment analysis, and crowdsourced
annotations
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
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