6,380 research outputs found
Quantifying Information Overload in Social Media and its Impact on Social Contagions
Information overload has become an ubiquitous problem in modern society.
Social media users and microbloggers receive an endless flow of information,
often at a rate far higher than their cognitive abilities to process the
information. In this paper, we conduct a large scale quantitative study of
information overload and evaluate its impact on information dissemination in
the Twitter social media site. We model social media users as information
processing systems that queue incoming information according to some policies,
process information from the queue at some unknown rates and decide to forward
some of the incoming information to other users. We show how timestamped data
about tweets received and forwarded by users can be used to uncover key
properties of their queueing policies and estimate their information processing
rates and limits. Such an understanding of users' information processing
behaviors allows us to infer whether and to what extent users suffer from
information overload.
Our analysis provides empirical evidence of information processing limits for
social media users and the prevalence of information overloading. The most
active and popular social media users are often the ones that are overloaded.
Moreover, we find that the rate at which users receive information impacts
their processing behavior, including how they prioritize information from
different sources, how much information they process, and how quickly they
process information. Finally, the susceptibility of a social media user to
social contagions depends crucially on the rate at which she receives
information. An exposure to a piece of information, be it an idea, a convention
or a product, is much less effective for users that receive information at
higher rates, meaning they need more exposures to adopt a particular contagion.Comment: To appear at ICSWM '1
Characterizing Attention Cascades in WhatsApp Groups
An important political and social phenomena discussed in several countries,
like India and Brazil, is the use of WhatsApp to spread false or misleading
content. However, little is known about the information dissemination process
in WhatsApp groups. Attention affects the dissemination of information in
WhatsApp groups, determining what topics or subjects are more attractive to
participants of a group. In this paper, we characterize and analyze how
attention propagates among the participants of a WhatsApp group. An attention
cascade begins when a user asserts a topic in a message to the group, which
could include written text, photos, or links to articles online. Others then
propagate the information by responding to it. We analyzed attention cascades
in more than 1.7 million messages posted in 120 groups over one year. Our
analysis focused on the structural and temporal evolution of attention cascades
as well as on the behavior of users that participate in them. We found specific
characteristics in cascades associated with groups that discuss political
subjects and false information. For instance, we observe that cascades with
false information tend to be deeper, reach more users, and last longer in
political groups than in non-political groups.Comment: Accepted as a full paper at the 11th International ACM Web Science
Conference (WebSci 2019). Please cite the WebSci versio
The Fake News Spreading Plague: Was it Preventable?
In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political
Speech and Real-time search" won the Best Paper Prize of the Web Science 2010
Conference. Among its findings were the discovery and documentation of what was
termed a "Twitter-bomb", an organized effort to spread misinformation about the
democratic candidate Martha Coakley through anonymous Twitter accounts. In this
paper, after summarizing the details of that event, we outline the recipe of
how social networks are used to spread misinformation. One of the most
important steps in such a recipe is the "infiltration" of a community of users
who are already engaged in conversations about a topic, to use them as organic
spreaders of misinformation in their extended subnetworks. Then, we take this
misinformation spreading recipe and indicate how it was successfully used to
spread fake news during the 2016 U.S. Presidential Election. The main
differences between the scenarios are the use of Facebook instead of Twitter,
and the respective motivations (in 2010: political influence; in 2016:
financial benefit through online advertising). After situating these events in
the broader context of exploiting the Web, we seize this opportunity to address
limitations of the reach of research findings and to start a conversation about
how communities of researchers can increase their impact on real-world societal
issues
Hoaxy: A Platform for Tracking Online Misinformation
Massive amounts of misinformation have been observed to spread in
uncontrolled fashion across social media. Examples include rumors, hoaxes, fake
news, and conspiracy theories. At the same time, several journalistic
organizations devote significant efforts to high-quality fact checking of
online claims. The resulting information cascades contain instances of both
accurate and inaccurate information, unfold over multiple time scales, and
often reach audiences of considerable size. All these factors pose challenges
for the study of the social dynamics of online news sharing. Here we introduce
Hoaxy, a platform for the collection, detection, and analysis of online
misinformation and its related fact-checking efforts. We discuss the design of
the platform and present a preliminary analysis of a sample of public tweets
containing both fake news and fact checking. We find that, in the aggregate,
the sharing of fact-checking content typically lags that of misinformation by
10--20 hours. Moreover, fake news are dominated by very active users, while
fact checking is a more grass-roots activity. With the increasing risks
connected to massive online misinformation, social news observatories have the
potential to help researchers, journalists, and the general public understand
the dynamics of real and fake news sharing.Comment: 6 pages, 6 figures, submitted to Third Workshop on Social News On the
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Modeling the structure and evolution of discussion cascades
We analyze the structure and evolution of discussion cascades in four popular
websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big
heterogeneities between these sites, a preferential attachment (PA) model with
bias to the root can capture the temporal evolution of the observed trees and
many of their statistical properties, namely, probability distributions of the
branching factors (degrees), subtree sizes and certain correlations. The
parameters of the model are learned efficiently using a novel maximum
likelihood estimation scheme for PA and provide a figurative interpretation
about the communication habits and the resulting discussion cascades on the
four different websites.Comment: 10 pages, 11 figure
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