39,362 research outputs found
Opinion modeling on social media and marketing aspects
We introduce and discuss kinetic models of opinion formation on social
networks in which the distribution function depends on both the opinion and the
connectivity of the agents. The opinion formation model is subsequently coupled
with a kinetic model describing the spreading of popularity of a product on the
web through a social network. Numerical experiments on the underlying kinetic
models show a good qualitative agreement with some measured trends of hashtags
on social media websites and illustrate how companies can take advantage of the
network structure to obtain at best the advertisement of their products
Emergence of Leadership in Communication
We study a neuro-inspired model that mimics a discussion (or information
dissemination) process in a network of agents. During their interaction, agents
redistribute activity and network weights, resulting in emergence of leader(s).
The model is able to reproduce the basic scenarios of leadership known in
nature and society: laissez-faire (irregular activity, weak leadership, sizable
inter-follower interaction, autonomous sub-leaders); participative or
democratic (strong leadership, but with feedback from followers); and
autocratic (no feedback, one-way influence). Several pertinent aspects of these
scenarios are found as well---e.g., hidden leadership (a hidden clique of
agents driving the official autocratic leader), and successive leadership (two
leaders influence followers by turns). We study how these scenarios emerge from
inter-agent dynamics and how they depend on behavior rules of agents---in
particular, on their inertia against state changes.Comment: 17 pages, 11 figure
Hy-DeFake: Hypergraph Neural Networks for Detecting Fake News in Online Social Networks
Nowadays social media is the primary platform for people to obtain news and
share information. Combating online fake news has become an urgent task to
reduce the damage it causes to society. Existing methods typically improve
their fake news detection performances by utilizing textual auxiliary
information (such as relevant retweets and comments) or simple structural
information (i.e., graph construction). However, these methods face two
challenges. First, an increasing number of users tend to directly forward the
source news without adding comments, resulting in a lack of textual auxiliary
information. Second, simple graphs are unable to extract complex relations
beyond pairwise association in a social context. Given that real-world social
networks are intricate and involve high-order relations, we argue that
exploring beyond pairwise relations between news and users is crucial for fake
news detection. Therefore, we propose constructing an attributed hypergraph to
represent non-textual and high-order relations for user participation in news
spreading. We also introduce a hypergraph neural network-based method called
Hy-DeFake to overcome the challenges. Our proposed method captures semantic
information from news content, credibility information from involved users, and
high-order correlations between news and users to learn distinctive embeddings
for fake news detection. The superiority of Hy-DeFake is demonstrated through
experiments conducted on four widely-used datasets, and it is compared against
six baselines using four evaluation metrics
Using privileged information to manipulate markets: insiders, gurus, and credibility
Access to private information is shown to generate both the incentives and the ability to manipulate asset markets through strategically distorted announcements. The fact that privileged information is noisy interferes with the public's attempts to learn whether such announcements are honest; it allows opportunistic individuals to manipulate prices repeatedly, without ever being fully found out. This leads us to extend Sobel's [1985] model of strategic communication to the case of noisy private signals. Our results show that when truthfulness is not easily verifiable, restrictions on trading by insiders may be needed to preserve the integrity of information embodied in prices
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