213,843 research outputs found
Weak ties: Subtle role of information diffusion in online social networks
As a social media, online social networks play a vital role in the social
information diffusion. However, due to its unique complexity, the mechanism of
the diffusion in online social networks is different from the ones in other
types of networks and remains unclear to us. Meanwhile, few works have been
done to reveal the coupled dynamics of both the structure and the diffusion of
online social networks. To this end, in this paper, we propose a model to
investigate how the structure is coupled with the diffusion in online social
networks from the view of weak ties. Through numerical experiments on
large-scale online social networks, we find that in contrast to some previous
research results, selecting weak ties preferentially to republish cannot make
the information diffuse quickly, while random selection can achieve this goal.
However, when we remove the weak ties gradually, the coverage of the
information will drop sharply even in the case of random selection. We also
give a reasonable explanation for this by extra analysis and experiments.
Finally, we conclude that weak ties play a subtle role in the information
diffusion in online social networks. On one hand, they act as bridges to
connect isolated local communities together and break through the local
trapping of the information. On the other hand, selecting them as preferential
paths to republish cannot help the information spread further in the network.
As a result, weak ties might be of use in the control of the virus spread and
the private information diffusion in real-world applications.Comment: Final version published in PR
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
Signed Link Analysis in Social Media Networks
Numerous real-world relations can be represented by signed networks with
positive links (e.g., trust) and negative links (e.g., distrust). Link analysis
plays a crucial role in understanding the link formation and can advance
various tasks in social network analysis such as link prediction. The majority
of existing works on link analysis have focused on unsigned social networks.
The existence of negative links determines that properties and principles of
signed networks are substantially distinct from those of unsigned networks,
thus we need dedicated efforts on link analysis in signed social networks. In
this paper, following social theories in link analysis in unsigned networks, we
adopt three social science theories, namely Emotional Information, Diffusion of
Innovations and Individual Personality, to guide the task of link analysis in
signed networks.Comment: In the 10th International AAAI Conference on Web and Social Media
(ICWSM-16
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