How Users' Personality Traits Predict Sentiment Tendencies of User-Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning.

Objective: Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user-generated content (UGC).

Method: We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies.

Exploring users' content creation and information dissemination behavior in social media: The moderating effect of social presence.

Users' personality traits reveal different social media behavior characteristics. In order to explore the intrinsic relationships between personality traits and social media behavior, this study analyzes the influence of users' personality traits on social media content creation and information dissemination behavior, as well as the moderating effect of social presence. We collect users' personality data via questionnaires, crawl social media behavior data of samples from social media sites, and then establish regression models to test the research hypotheses.

[The change in IL-13 mRNA expression in rat lungs with acute pulmonary injury induced by lipopolysaccharide].

Objective: To observe the change in IL-13 mRNA expression in rat lungs with acute lung injury (ALI) induced by lipopolysaccharide (LPS), so as to investigate the significance of anti-inflammatory mechanism in the development of ALI.

Methods: (1) Different doses (2 approximately 8 mg/kg) of LPS were injected via the tail vein to the rats to establish ALI model. (2) The IL-13 mRNA content in pulmonary tissue was determined by RT-PCR.