This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers' judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods.

翻译：本研究通过语义网络分析探讨在线新闻对社会经济消费者感知的影响。我们利用涵盖四年时间的180多万篇意大利媒体在线文章，计算特定经济相关关键词的语义重要性，以考察文章中出现的词汇是否能够预测消费者对经济状况的判断以及消费者信心指数。我们采用了一种创新性的方法分析大规模文本数据，结合了文本挖掘与社会网络分析的方法和工具。研究结果表明，该方法对当前家庭与国家状况的判断具有强大的预测能力。我们提出的指标为估算消费者信心提供了一种补充途径，减少了传统调查方法存在的局限性。