5,610 research outputs found
Topology comparison of Twitter diffusion networks effectively reveals misleading information
In recent years, malicious information had an explosive growth in social
media, with serious social and political backlashes. Recent important studies,
featuring large-scale analyses, have produced deeper knowledge about this
phenomenon, showing that misleading information spreads faster, deeper and more
broadly than factual information on social media, where echo chambers,
algorithmic and human biases play an important role in diffusion networks.
Following these directions, we explore the possibility of classifying news
articles circulating on social media based exclusively on a topological
analysis of their diffusion networks. To this aim we collected a large dataset
of diffusion networks on Twitter pertaining to news articles published on two
distinct classes of sources, namely outlets that convey mainstream, reliable
and objective information and those that fabricate and disseminate various
kinds of misleading articles, including false news intended to harm, satire
intended to make people laugh, click-bait news that may be entirely factual or
rumors that are unproven. We carried out an extensive comparison of these
networks using several alignment-free approaches including basic network
properties, centrality measures distributions, and network distances. We
accordingly evaluated to what extent these techniques allow to discriminate
between the networks associated to the aforementioned news domains. Our results
highlight that the communities of users spreading mainstream news, compared to
those sharing misleading news, tend to shape diffusion networks with subtle yet
systematic differences which might be effectively employed to identify
misleading and harmful information.Comment: A revised new version is available on Scientific Report
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
PopRank: Ranking pages' impact and users' engagement on Facebook
Users online tend to acquire information adhering to their system of beliefs
and to ignore dissenting information. Such dynamics might affect page
popularity. In this paper we introduce an algorithm, that we call PopRank, to
assess both the Impact of Facebook pages as well as users' Engagement on the
basis of their mutual interactions. The ideas behind the PopRank are that i)
high impact pages attract many users with a low engagement, which means that
they receive comments from users that rarely comment, and ii) high engagement
users interact with high impact pages, that is they mostly comment pages with a
high popularity. The resulting ranking of pages can predict the number of
comments a page will receive and the number of its posts. Pages impact turns
out to be slightly dependent on pages' informative content (e.g., science vs
conspiracy) but independent of users' polarization.Comment: 10 pages, 5 figure
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
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