About this Research Topic
As social media have made accessible the message content of an increasing proportion of human communication, the unstructured text data provides the basis for inferring message contexts and subtexts that reveal the framing of content creators and the subjective opinions and judgments of receivers. Although this form of data is challenging to analyze and interpret, it can deliver a more comprehensive and holistic understanding of communication than structured data. As semantic network analysis methods have evolved over the past 50 years, how the links among words are defined and the algorithms used for the automated analyses of text, visualization of semantic patterns, and statistical modeling have diversified.
The goal of this journal issue is to present readers with an array of current issues, concepts, and methods for the semantic network analysis of text that point to promising paths for future research on social media. As social media have come under increasing political scrutiny, new approaches to the analysis of its content and effects not only advance theory but have policy significance for potentially mitigating some of the medium’s negative effects and increasing its benefits. Submissions are welcome that frame theoretical, analytical, and policy questions regarding social media and address them with various supervised and unsupervised methods for text analysis.
The scope of relevant topics is broad and includes but is not limited to:
● Content moderation
● Polarization
● Relationships between social media and mainstream media processes
● Misinformation
● Influencer processes and effects
● Social media effects at various levels of analysis
● Social movements and campaigns
● Collective intelligence and knowledge creation
● Social comparison
● Issue framing
● Public opinion
● Political processes
● Effects of social media
● Comparative analysis of social media across platforms
● Social media and development
● Cross-cultural analysis
● Crisis management and communication
● Dynamics of social media discourse
● Use of social media by governments, businesses, and groups to achieve goals
● Methodological and analytical issues
● Combining semantic network analysis approaches with other research methods for examining social media texts and effects
The goal of this journal issue is to present readers with an array of current issues, concepts, and methods for the semantic network analysis of text that point to promising paths for future research on social media. As social media have come under increasing political scrutiny, new approaches to the analysis of its content and effects not only advance theory but have policy significance for potentially mitigating some of the medium’s negative effects and increasing its benefits. Submissions are welcome that frame theoretical, analytical, and policy questions regarding social media and address them with various supervised and unsupervised methods for text analysis.
The scope of relevant topics is broad and includes but is not limited to:
● Content moderation
● Polarization
● Relationships between social media and mainstream media processes
● Misinformation
● Influencer processes and effects
● Social media effects at various levels of analysis
● Social movements and campaigns
● Collective intelligence and knowledge creation
● Social comparison
● Issue framing
● Public opinion
● Political processes
● Effects of social media
● Comparative analysis of social media across platforms
● Social media and development
● Cross-cultural analysis
● Crisis management and communication
● Dynamics of social media discourse
● Use of social media by governments, businesses, and groups to achieve goals
● Methodological and analytical issues
● Combining semantic network analysis approaches with other research methods for examining social media texts and effects
Keywords: Semantic Network Analysis, Social-Semantic Network Analysis, Topic Network Analysis, Social Media discourse, Content Analysis, text analysis, word co-occurrence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
