Diachronic profile of startup companies through social media

Ana Rita Peixoto, Ana de Almeida, Nuno António, Fernando Batista, Ricardo Ribeiro

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Social media platforms have become powerful tools for startups, helping them find customers and raise funding. In this study, we applied a social media intelligence-based methodology to analyze startups’ content and to understand how their communication strategies may differ during their scaling process. To understand if a startup’s social media content reflects its current business maturation position, we first defined an adequate life cycle model for startups based on funding rounds and product maturity. Using Twitter as the source of information and selecting a sample of known Portuguese IT startups at different phases of their life cycle, we analyzed their Twitter data. After preprocessing the data, using latent Dirichlet allocation, topic modeling techniques enabled the categorization of the data according to the topics arising in the published contents of the startups, making it possible to discover that contents can be grouped into five specific topics: “Fintech and ML,” “IT,” “Business Operations,” “Product/Service R&D,” and “Bank and Funding.” By comparing those profiles against the startup’s life cycle, we were able to understand how contents change over time. This provided a diachronic profile for each company, showing that while certain topics remain prevalent in the startup’s scaling, others depend on a particular phase of the startup’s cycle. Our analysis revealed that startups’ social media content differs along their life cycle, highlighting the importance of understanding how startups use social media at different stages of their development.
Original languageEnglish
Article number52
Pages (from-to)1-18
Number of pages18
JournalSocial Network Analysis and Mining
Issue number1
Publication statusAccepted/In press - 18 Mar 2023


  • Topic modeling
  • Social media
  • Startups
  • Life cycle model
  • Twitter data


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