This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,t...This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,this research develops a systematic analytical framework that integrates techniques such as propensity score matching,regression analysis,and regression discontinuity design to identify the causal effects of content characteristics,user attributes,and social network structures on user interactions,including clicks,shares,comments,and likes.The empirical findings indicate that factors such as sentiment,topical relevance,and network centrality have significant causal impacts on user behavior,with notable differences observed among various user groups.This study not only enriches the theoretical understanding of social media data analysis but also provides data-driven decision support and practical guidance for fields such as digital marketing,public opinion management,and digital governance.展开更多
Background: The use of social media platforms for health and nutrition information has become popular among college students. Although social media made information readily accessible in different formats, nutritional...Background: The use of social media platforms for health and nutrition information has become popular among college students. Although social media made information readily accessible in different formats, nutritional misinformation promoted by influencers and non-experts caused negative impact on diet behavior and perception of body image. Previous research indicated that extensive use of social media was positively linked to disordered eating behaviors. Social media platforms like Facebook and Instagram that allow users to follow celebrities intensified exposure to influencers’ messages and images and resulted in negative moods and body dissatisfaction. Objective: This paper aims to explore the impact of social media on college students’ dietary behaviors and body image. Participants: 18 undergraduate students from a public university in the Southern United States were recruited through a mass email. Methods: A cross-sectional qualitative study of three focus groups was conducted. The focus groups were based on guiding open-ended questions. Atlas.ti was used to code and analyze the data using inductive and deductive codes. Results: Three main themes were identified. The conditions theme included elements that explain why and how social media influences the participants’ actions. The actions theme included eating behavior, physical activity, and dietary supplement intake. The consequences theme describes anticipated or actual outcomes of actions such as body image and ideal weight. Conclusions: Social media has had a negative influence on diet behaviors and a positive influence on physical activity. Evidence-based nutrition and weight management information is needed to thwart potential misinformation.展开更多
As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain ...As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.展开更多
Purpose: We aim to create a model of consumer health information seeking behavior via social media, then to have a better understand of it. After that further efforts should be made to provide targeted recommendations...Purpose: We aim to create a model of consumer health information seeking behavior via social media, then to have a better understand of it. After that further efforts should be made to provide targeted recommendations for media managers to promote health communication via social media. Methods: Our custom model was derived from literature review, empirical research was tested by the use of questionnaire investigation, and then the collected data were analyzed by structural equation model tool SmartPLS. Finally, the custom model was modified according to the experimental results of SmartPLS. Results: A total of 239 (66.39%) of the respondents were female and 121 (33.61%) were male. The maximum of two stages of age were 18 - 29 (70.56%), 30 - 39 (13.89%). Wechat (60.28%), QQ Zone (55.22%), Micro-blog (48.89%) were the most commonly used social media to obtain health information. About 44.72% of respondents used social media to obtain health information multiple times a day. The whole numerical values of AVE, cronbach’s alpha, CR and square roots of AVE were above the benchmarks of themselves and showed great reliability and validity. All the 11 hypotheses had obvious statistical significance, the P-value of eight path coefficients exhibited <0.001, one path coefficient exhibited <0.01 and two path coefficients exhibited <0.05. Conclusions: A suitable model of consumer health information seeking behavior via social media was created and some inner relationships were found. Namely, gratification of health information and its platform had a positive effect on attitudes toward health information seeking behavior. Health information literacy and health status were proved to have a significant influence on attitudes toward health information seeking behavior, subject norms and perceived behavioral control respectively. In addition, attitudes toward the health information seeking behavior, subject norms and perceived behavioral control were proved to positively associate with health information seeking behavior intention.展开更多
User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that tak...User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidi- mensional algorithm is more accurate than a traditional recom- mendation algorithm.展开更多
This study employs causal inference methods to analyze user behavior on short-video platforms,examining how content characteristics,algorithmic recommendations,and social networks impact engagement.Using Propensity Sc...This study employs causal inference methods to analyze user behavior on short-video platforms,examining how content characteristics,algorithmic recommendations,and social networks impact engagement.Using Propensity Score Matching(PSM),Regression Discontinuity Design(RDD),and Instrumental Variables(IV),findings indicate that algorithmic promotion significantly boosts content diffusion,emotionally charged content is more shareable than neutral content,and influencer interactions increase visibility by 80%.The study shows that platform algorithms shape both information flow and group psychology.The results offer insights for social media marketing,public opinion management,and digital governance,with policy recommendations for content diversity,platform transparency,and algorithm fairness.展开更多
Along with the development of socialized media and self-help tourism,tourism industry has been going into tourism social times.Based on technology acceptance model,use and gratifications approach,and weighted and calc...Along with the development of socialized media and self-help tourism,tourism industry has been going into tourism social times.Based on technology acceptance model,use and gratifications approach,and weighted and calculated needs theory,this study explored the impact of perceived popularity,perceived characteristics,and perceived need on the use of tourism social network site and being a member of it.This study also discussed the interaction of perceived popularity,perceived characteristics,and perceived need.The findings of this paper could be used to help the management operator pay attention to strengthen the function of tourism social network site in order to provide better information for users and satisfied the needs of users.展开更多
The article tries to discover the major authors in the field of information seeking behavior via social network analysis. It is to be accomplished through a literature review and also by focusing on a graphic map show...The article tries to discover the major authors in the field of information seeking behavior via social network analysis. It is to be accomplished through a literature review and also by focusing on a graphic map showing the seven most productive coauthors in this field. Based on these seven authors' work, five probable research directions about information seeking behavior are discerned and presented.展开更多
文摘This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,this research develops a systematic analytical framework that integrates techniques such as propensity score matching,regression analysis,and regression discontinuity design to identify the causal effects of content characteristics,user attributes,and social network structures on user interactions,including clicks,shares,comments,and likes.The empirical findings indicate that factors such as sentiment,topical relevance,and network centrality have significant causal impacts on user behavior,with notable differences observed among various user groups.This study not only enriches the theoretical understanding of social media data analysis but also provides data-driven decision support and practical guidance for fields such as digital marketing,public opinion management,and digital governance.
文摘Background: The use of social media platforms for health and nutrition information has become popular among college students. Although social media made information readily accessible in different formats, nutritional misinformation promoted by influencers and non-experts caused negative impact on diet behavior and perception of body image. Previous research indicated that extensive use of social media was positively linked to disordered eating behaviors. Social media platforms like Facebook and Instagram that allow users to follow celebrities intensified exposure to influencers’ messages and images and resulted in negative moods and body dissatisfaction. Objective: This paper aims to explore the impact of social media on college students’ dietary behaviors and body image. Participants: 18 undergraduate students from a public university in the Southern United States were recruited through a mass email. Methods: A cross-sectional qualitative study of three focus groups was conducted. The focus groups were based on guiding open-ended questions. Atlas.ti was used to code and analyze the data using inductive and deductive codes. Results: Three main themes were identified. The conditions theme included elements that explain why and how social media influences the participants’ actions. The actions theme included eating behavior, physical activity, and dietary supplement intake. The consequences theme describes anticipated or actual outcomes of actions such as body image and ideal weight. Conclusions: Social media has had a negative influence on diet behaviors and a positive influence on physical activity. Evidence-based nutrition and weight management information is needed to thwart potential misinformation.
文摘As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.
文摘Purpose: We aim to create a model of consumer health information seeking behavior via social media, then to have a better understand of it. After that further efforts should be made to provide targeted recommendations for media managers to promote health communication via social media. Methods: Our custom model was derived from literature review, empirical research was tested by the use of questionnaire investigation, and then the collected data were analyzed by structural equation model tool SmartPLS. Finally, the custom model was modified according to the experimental results of SmartPLS. Results: A total of 239 (66.39%) of the respondents were female and 121 (33.61%) were male. The maximum of two stages of age were 18 - 29 (70.56%), 30 - 39 (13.89%). Wechat (60.28%), QQ Zone (55.22%), Micro-blog (48.89%) were the most commonly used social media to obtain health information. About 44.72% of respondents used social media to obtain health information multiple times a day. The whole numerical values of AVE, cronbach’s alpha, CR and square roots of AVE were above the benchmarks of themselves and showed great reliability and validity. All the 11 hypotheses had obvious statistical significance, the P-value of eight path coefficients exhibited <0.001, one path coefficient exhibited <0.01 and two path coefficients exhibited <0.05. Conclusions: A suitable model of consumer health information seeking behavior via social media was created and some inner relationships were found. Namely, gratification of health information and its platform had a positive effect on attitudes toward health information seeking behavior. Health information literacy and health status were proved to have a significant influence on attitudes toward health information seeking behavior, subject norms and perceived behavioral control respectively. In addition, attitudes toward the health information seeking behavior, subject norms and perceived behavioral control were proved to positively associate with health information seeking behavior intention.
文摘User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidi- mensional algorithm is more accurate than a traditional recom- mendation algorithm.
文摘This study employs causal inference methods to analyze user behavior on short-video platforms,examining how content characteristics,algorithmic recommendations,and social networks impact engagement.Using Propensity Score Matching(PSM),Regression Discontinuity Design(RDD),and Instrumental Variables(IV),findings indicate that algorithmic promotion significantly boosts content diffusion,emotionally charged content is more shareable than neutral content,and influencer interactions increase visibility by 80%.The study shows that platform algorithms shape both information flow and group psychology.The results offer insights for social media marketing,public opinion management,and digital governance,with policy recommendations for content diversity,platform transparency,and algorithm fairness.
基金This study was supported by a grant from the Projects of the National Natural Science Foundation of China(No.72074053).
文摘Along with the development of socialized media and self-help tourism,tourism industry has been going into tourism social times.Based on technology acceptance model,use and gratifications approach,and weighted and calculated needs theory,this study explored the impact of perceived popularity,perceived characteristics,and perceived need on the use of tourism social network site and being a member of it.This study also discussed the interaction of perceived popularity,perceived characteristics,and perceived need.The findings of this paper could be used to help the management operator pay attention to strengthen the function of tourism social network site in order to provide better information for users and satisfied the needs of users.
文摘The article tries to discover the major authors in the field of information seeking behavior via social network analysis. It is to be accomplished through a literature review and also by focusing on a graphic map showing the seven most productive coauthors in this field. Based on these seven authors' work, five probable research directions about information seeking behavior are discerned and presented.
