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Modeling communication asymmetry and content personalization in online social networks
The increasing popularity of online social networks (OSNs) attracted growing
interest in modeling social interactions. On online social platforms, a few
individuals, commonly referred to as influencers, produce the majority of
content consumed by users and hegemonize the landscape of the social debate.
However, classical opinion models do not capture this communication asymmetry.
We develop an opinion model inspired by observations on social media platforms
{with two main objectives: first, to describe this inherent communication
asymmetry in OSNs, and second, to model the effects of content personalization.
We derive a Fokker-Planck equation for the temporal evolution of users' opinion
distribution and analytically characterize the stationary system behavior.
Analytical results, confirmed by Monte-Carlo simulations, show how strict forms
of content personalization tend to radicalize user opinion, leading to the
emergence of echo chambers, and favor structurally advantaged influencers. As
an example application, we apply our model to Facebook data during the Italian
government crisis in the summer of 2019. Our work provides a flexible framework
to evaluate the impact of content personalization on the opinion formation
process, focusing on the interaction between influential individuals and
regular users. This framework is interesting in the context of marketing and
advertising, misinformation spreading, politics and activism
Topicality and Social Impact: Diverse Messages but Focused Messengers
Are users who comment on a variety of matters more likely to achieve high
influence than those who delve into one focused field? Do general Twitter
hashtags, such as #lol, tend to be more popular than novel ones, such as
#instantlyinlove? Questions like these demand a way to detect topics hidden
behind messages associated with an individual or a hashtag, and a gauge of
similarity among these topics. Here we develop such an approach to identify
clusters of similar hashtags by detecting communities in the hashtag
co-occurrence network. Then the topical diversity of a user's interests is
quantified by the entropy of her hashtags across different topic clusters. A
similar measure is applied to hashtags, based on co-occurring tags. We find
that high topical diversity of early adopters or co-occurring tags implies high
future popularity of hashtags. In contrast, low diversity helps an individual
accumulate social influence. In short, diverse messages and focused messengers
are more likely to gain impact.Comment: 9 pages, 7 figures, 6 table
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