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Natural Language Content Mediates the Association Between Active Interactions on Social Network Services and
Kazuma Mori1,2, Hugo Hadjur1, Masahiko Haruno1,3
1Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Japan.
Social networking service (SNS) interaction frequency correlates with well-being. Positive language boosts well-being for active users, while worrying language lowers it for less active users, explaining these differences.
Area of Science:
- Psychology
- Computational Social Science
- Natural Language Processing
Background:
- Prior research links active social networking service (SNS) use to enhanced subjective well-being (SWB).
- The influence of semantic content on the relationship between SNS interaction and SWB remains underexplored.
Purpose of the Study:
- To investigate how the semantic content of SNS interactions mediates the link between interaction frequency and SWB.
- To analyze the role of word sentiment in the relationship between Twitter activity and user well-being.
Main Methods:
- Utilized natural language processing (NLP) and mediation analysis on Twitter data from 217 participants.
- Correlated the frequency of active Twitter interactions (replies) with SWB scores.
- Examined the mediating effects of positive, negative, and worrying word usage on the interaction-SWB relationship.
Main Results:
- Higher frequency of active Twitter interactions positively correlated with SWB.
- Positive word usage mediated the positive association between high interaction and SWB.
- Worrying word usage mediated the negative association between low interaction and SWB.
Conclusions:
- Semantic content, specifically positive and worrying language, explains the differential impact of SNS interaction levels on SWB.
- NLP analysis reveals mechanisms underlying the connection between online social behavior and psychological well-being.
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