homogenous. The open comment interaction
environment sets up controversial topics, and the
emotional reactions of users are inconsistent with
their cognitive expectations (Chen et al, 2002). When
faced with a large amount of information, users may
feel confused and tired, and it is difficult to discern
which information is true and reliable. This
information overload not only affects the user
experience but also may lead to a decrease in user
dependence on the platform.
3.2.2 Limitations of Algorithm
Recommendations
The algorithm recommendation mechanism may lead
to the information cocoon effect, where users can
only see the content they are interested in, limiting the
diversity of information. By tailoring content to
individual preferences, algorithms can reinforce
existing beliefs and attitudes, potentially leading to
greater polarization and a lack of exposure to
alternative viewpoints (Jia & Zhang, 2020). Although
this personalized recommendation mechanism
improves the user's viewing experience, it may also
cause users to fall into an information cocoon, and it
is difficult to access different views and information.
3.2.3 Spreading Negative Information
Negative information and false information on short
video platforms are easy to spread quickly, affecting
the healthy development of public opinion. The
content posted by users on the platform may have
problems such as false information, negative
emotions, vulgar content, etc. The spread of negative
information through personalized recommendations
can have adverse mental health impacts on users,
including increased anxiety and stress. The constant
exposure to sensationalized and emotionally charged
content can contribute to a heightened state of
emotional arousal, which may lead to chronic stress
and anxiety disorders (Jia & Zhang, 2020). The rapid
spread of this negative information not only affects
the healthy development of public opinion but also
may lead to the intensification of social
contradictions.
3.2.4 User Privacy Issues
When short video platforms collect and use user data,
there is a risk of user privacy disclosure. To provide
personalized recommendations and advertising,
platforms need to collect a large amount of data about
users, including viewing history, interest preferences,
social relationships, and so on. The collection and use
of these data have the risk of user privacy disclosure,
which may affect the security of users' personal
information. TikTok, like many other social media
platforms, shares user data with third-party
advertisers and partners, which can lead to privacy
risks and potential data breaches (Spiekermann &
Schreck, 2020).
4 SUGGESTION
4.1 Recommendations for Policy
Makers
Policymakers should formulate and improve relevant
laws and regulations, strengthen supervision of short
video platforms, and ensure the healthy and orderly
dissemination of information. A comprehensive
regulatory framework is essential to balance
innovation and user safety. This framework should
clear both national and international regulations,
industry standards, and platform-specific policies.
Policy makers should clarify the responsibilities and
obligations of platforms, strengthen the supervision
of platform content, and ensure the authenticity and
legitimacy of platform content (VanDerWerff &
Hancock, 2020). At the same time, policymakers
should establish a cross-departmental supervision
mechanism to ensure the effectiveness and
coordination of supervision.
Policymakers should encourage short video
platforms to establish an industry self-discipline
mechanism and strengthen self-regulation and self-
restraint. Through organizations such as industry
associations, policymakers can promote the platform
to establish industry standards and self-discipline
mechanisms to jointly maintain the healthy
development of the industry. At the same time,
policymakers should strengthen the supervision of the
industry self-regulatory mechanism to ensure the
effectiveness and fairness of the self-regulatory
mechanism.
4.2 Recommendations for Platform
Operators
4.2.1 Optimization Algorithm
Recommendation Mechanism
Platform operators should optimize the algorithm
recommendation mechanism, balance personalized
recommendation and information diversity, and
reduce the information cocoon effect. The platform
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