๐๐๐๐๐๐ญ๐ข๐ฏ๐ ๐๐๐๐ข๐ฌ๐ข๐จ๐ง-๐๐๐ค๐ข๐ง๐ ๐๐ง๐ ๐๐๐ซ๐ฉ๐ฅ๐๐ฑ๐ข๐ญ๐ฒ ๐ข๐ง ๐๐ข๐ ๐ข๐ญ๐๐ฅ ๐๐๐๐ข๐ ๐๐จ๐๐ข๐๐ฅ ๐๐๐ก๐๐ฏ๐ข๐จ๐ซ ๐๐๐ฌ๐๐ ๐จ๐ง ๐๐๐ ๐๐ง๐ ๐๐๐๐
doi.org/10.1142/S01291564254โฆ
Why should you read this research article?
๐นAddresses the growing challenge of emotionally-driven, "boundedly rational" privacy decision-making on
#DigitalMedia platforms, where users struggle to weigh privacy risks against social benefits โ proposing a framework that integrates
#DualProcessTheory, privacy communication management theory, and
#RoleTheory to model these complex behaviours.
๐นLeverages
#NLP and
#CLIP (
#ContrastiveLanguageImagePretraining) in tandem to perform cross-modal
#SentimentAnalysis across text and image data harvested from platforms such as Weibo, WeChat, and Instagram โ enabling the construction of accurate user behavioural portraits that capture interests, preferences, and emotional tendencies.
๐นConstructs three interconnected research models examining how role conflict, emotional valence, and privacy boundary formation interact to shape users' self-disclosure decisions on
#SocialMedia โ validated through a series of four 2ร2 laboratory experiments involving simulation trials, interview trials, and data comparisons.
๐นCombines
#BERT,
#Word2Vec,
#CNN,
#LSTM, and CLIP within a unified text classification pipeline to perform multi-class sentiment analysis on privacy policy texts, classifying user emotions into positive, negative, and neutral categories and enabling automated evaluation of privacy compliance content.
๐นDemonstrates that the proposed NLP-CLIP model achieves an optimal solution probability of 0.96 โ matching industry-leading models โ whilst significantly improving computational speed and decision evaluation efficiency under limited hardware resources, outperforming benchmark models on CFI and NNFI metrics.
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