Social Media Analysis in Food Consumer Behavior
Summary
Social media analysis has emerged as a vital tool for understanding how consumers perceive, discuss and choose food products in real time. By mining user-generated content across platforms such as Twitter, forums and review sites, researchers can track emerging trends, gauge sentiment towards ingredients and brands, and identify the drivers of dietary choices. Methods span from natural language processing pipelines that extract thematic features to network analysis techniques that reveal influencer communities and information diffusion patterns. This interdisciplinary field draws on computational linguistics, marketing science and public health to interpret vast quantities of unstructured data, offering insights into taste preferences, health perceptions, sustainability concerns and crisis responses. Practical applications include targeted marketing campaigns, rapid risk communication during food safety incidents and the design of healthier product formulations. Yet challenges remain in ensuring data representativeness, accounting for platform-specific biases and safeguarding user privacy. As machine-learning models grow more sophisticated and as multimodal data sources (text, image, video) become available, the capacity to predict consumer behaviour and to tailor interventions for global food systems is set to deepen.
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Social Media Analysis in Food Consumer Behavior publication trend
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Technical terms
Natural language processing (NLP): Computational techniques for analysing human language to extract meaning, categories and structure from text.
Content analysis: Systematic coding and interpretation of textual or multimedia data to identify themes, patterns and frequencies.
Sentiment analysis: Automated classification of text into affective polarities (positive, negative, neutral) to measure public opinion.
Word embedding: Method of representing words as continuous-valued vectors based on context, capturing semantic relationships.
Network analysis: Quantitative examination of relationships among entities (users, hashtags or terms) to reveal structural and diffusion patterns.
References
- Identification and Analysis of Strawberries’ Consumer Opinions on Twitter for Marketing Purposes. Agronomy (2021).
- Automated Text Analysis Based on Skip‐Gram Model for Food Evaluation in Predicting Consumer Acceptance. Computational Intelligence and Neuroscience (2018).
- Infant food users' perceptions of safety: A web-based analysis approach. Frontiers in Artificial Intelligence (2023).
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