655 research outputs found
From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection
The COVID-19 pandemic has gained worldwide attention and allowed fake news,
such as ``COVID-19 is the flu,'' to spread quickly and widely on social media.
Combating this coronavirus infodemic demands effective methods to detect fake
news. To this end, we propose a method to infer news credibility from hashtags
involved in news dissemination on social media, motivated by the tight
connection between hashtags and news credibility observed in our empirical
analyses. We first introduce a new graph that captures all (direct and
\textit{indirect}) relationships among hashtags. Then, a language-independent
semi-supervised algorithm is developed to predict fake news based on this
constructed graph. This study first investigates the indirect relationship
among hashtags; the proposed approach can be extended to any homogeneous graph
to capture a comprehensive relationship among nodes. Language independence
opens the proposed method to multilingual fake news detection. Experiments
conducted on two real-world datasets demonstrate the effectiveness of our
approach in identifying fake news, especially at an \textit{early} stage of
propagation
Sentiment Paradoxes in Social Networks: Why Your Friends Are More Positive Than You?
Most people consider their friends to be more positive than themselves,
exhibiting a Sentiment Paradox. Psychology research attributes this paradox to
human cognition bias. With the goal to understand this phenomenon, we study
sentiment paradoxes in social networks. Our work shows that social connections
(friends, followees, or followers) of users are indeed (not just illusively)
more positive than the users themselves. This is mostly due to positive users
having more friends. We identify five sentiment paradoxes at different network
levels ranging from triads to large-scale communities. Empirical and
theoretical evidence are provided to validate the existence of such sentiment
paradoxes. By investigating the relationships between the sentiment paradox and
other well-developed network paradoxes, i.e., friendship paradox and activity
paradox, we find that user sentiments are positively correlated to their number
of friends but rarely to their social activity. Finally, we demonstrate how
sentiment paradoxes can be used to predict user sentiments.Comment: The 14th International AAAI Conference on Web and Social Media (ICWSM
2020
Some asymptotic stationary point theorems in topological spaces
AbstractIn this paper, we present some asymptotic stationary point results for topological contraction mappings by relaxing the compactness of the space. Moreover, some classes of topological contractions are characterized
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