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

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  • Lipizzi, Carlo
  • Iandoli, Luca
  • Ramirez Marquez, José Emmanuel

Abstract

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 72h 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.

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  • Lipizzi, Carlo & Iandoli, Luca & Ramirez Marquez, José Emmanuel, 2015. "Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams," International Journal of Information Management, Elsevier, vol. 35(4), pages 490-503.
    • Handle: RePEc:eee:ininma:v:35:y:2015:i:4:p:490-503
    DOI: 10.1016/j.ijinfomgt.2015.04.001
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    7. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
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    Cited by:

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    4. Aswani, Reema & Kar, Arpan Kumar & Ilavarasan, P. Vigneswara & Dwivedi, Yogesh K., 2018. "Search engine marketing is not all gold: Insights from Twitter and SEOClerks," International Journal of Information Management, Elsevier, vol. 38(1), pages 107-116.
    5. Nisar, Tahir M. & Prabhakar, Guru & Patil, Pushp P., 2018. "Sports clubs’ use of social media to increase spectator interest," International Journal of Information Management, Elsevier, vol. 43(C), pages 188-195.
    6. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.
    7. Amal Almansour & Reem Alotaibi & Hajar Alharbi, 2022. "Text-rating review discrepancy (TRRD): an integrative review and implications for research," Future Business Journal, Springer, vol. 8(1), pages 1-15, December.
    8. Jose Ramon Saura & Daniel Palacios-Marqués & Domingo Ribeiro-Soriano, 2023. "Leveraging SMEs technologies adoption in the Covid-19 pandemic: a case study on Twitter-based user-generated content," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1696-1722, October.
    9. Jamali, Mehdi & Nejat, Ali & Ghosh, Souparno & Jin, Fang & Cao, Guofeng, 2019. "Social media data and post-disaster recovery," International Journal of Information Management, Elsevier, vol. 44(C), pages 25-37.

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