  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.
