Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams

• Reactions to new products by Apple and Samsung are assessed analyzing Twitter streams.
• Streams are modeled as virtual conversations, generating dynamically updated concept maps.
• Using topological analysis we identify patterns of what people say and how they talk.
• Apple conversation is less fragmented, contains more topics, and less negative sentiment.

In this paper we use Twitter data to assess customers early reactions to the launch of two new products by Apple and Samsung by analyzing the streams generated in a 72 h window around the two events. We present a methodology based on conversational analysis to extract concept maps from Twitter streams and use semantic and topological metrics to compare the conversations. Our findings show that there are significant differences in the structural patterns of the two conversations and that the analysis of these differences can be highly informative about early customers perceptions and value judgments associated with the competing products.