677 research outputs found
Extroverts Tweet Differently from Introverts in Weibo
Being dominant factors driving the human actions, personalities can be
excellent indicators in predicting the offline and online behavior of different
individuals. However, because of the great expense and inevitable subjectivity
in questionnaires and surveys, it is challenging for conventional studies to
explore the connection between personality and behavior and gain insights in
the context of large amount individuals. Considering the more and more
important role of the online social media in daily communications, we argue
that the footprint of massive individuals, like tweets in Weibo, can be the
inspiring proxy to infer the personality and further understand its functions
in shaping the online human behavior. In this study, a map from self-reports of
personalities to online profiles of 293 active users in Weibo is established to
train a competent machine learning model, which then successfully identifies
over 7,000 users as extroverts or introverts. Systematical comparisons from
perspectives of tempo-spatial patterns, online activities, emotion expressions
and attitudes to virtual honor surprisingly disclose that the extrovert indeed
behaves differently from the introvert in Weibo. Our findings provide solid
evidence to justify the methodology of employing machine learning to
objectively study personalities of massive individuals and shed lights on
applications of probing personalities and corresponding behaviors solely
through online profiles.Comment: Datasets of this study can be freely downloaded through:
https://doi.org/10.6084/m9.figshare.4765150.v
Multi-Dimensional Recommendation Scheme for Social Networks Considering a User Relationship Strength Perspective
Developing a computational method based on user relationship strength for multi-dimensional recommendation is a significant challenge. The traditional recommendation methods have relatively low accuracy because they lack considering information from the perspective of user relationship strength into the recommendation algorithm. User relationship strength reflects the degree of closeness between two users, which can make the recommendation system more efficient between users in pairs. This paper proposes a multi-dimensional comprehensive recommendation method based on user relationship strength. We take three main factors into consideration, including the strength of user relationship, the similarity of entities, and the degree of user interest. First, we introduce a novel method to generate a user candidate set and an entity candidate set by calculating the relationship strength between two users and the similarity between two entities. Then, the algorithm will calculate the user interest degree of each user in the user candidate set to each entity in the entity candidate set, if the user interest degree is larger than or equal to a threshold, this particular entity will be recommended to this user. The performance of the proposed method was verified based on the real-world social network dataset and the e-commerce website dataset, and the experimental result suggests that this method can improve the recommendation accuracy
No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation
Online Social Network (OSN) has become a hotbed of fake news due to the low
cost of information dissemination. Although the existing methods have made many
attempts in news content and propagation structure, the detection of fake news
is still facing two challenges: one is how to mine the unique key features and
evolution patterns, and the other is how to tackle the problem of small samples
to build the high-performance model. Different from popular methods which take
full advantage of the propagation topology structure, in this paper, we propose
a novel framework for fake news detection from perspectives of semantic,
emotion and data enhancement, which excavates the emotional evolution patterns
of news participants during the propagation process, and a dual deep
interaction channel network of semantic and emotion is designed to obtain a
more comprehensive and fine-grained news representation with the consideration
of comments. Meanwhile, the framework introduces a data enhancement module to
obtain more labeled data with high quality based on confidence which further
improves the performance of the classification model. Experiments show that the
proposed approach outperforms the state-of-the-art methods
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