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Netw. Anal. Min."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2019 content and to understand how their communication strategies may differ during their scaling process. To understand if a startup\u2019s 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: \u201cFintech and ML,\u201d \u201cIT,\u201d \u201cBusiness Operations,\u201d \u201cProduct\/Service R&amp;D,\u201d and \u201cBank and Funding.\u201d By comparing those profiles against the startup\u2019s 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\u2019s scaling, others depend on a particular phase of the startup\u2019s cycle. Our analysis revealed that startups\u2019 social media content differs along their life cycle, highlighting the importance of understanding how startups use social media at different stages of their development.<\/jats:p>","DOI":"10.1007\/s13278-023-01055-2","type":"journal-article","created":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T02:02:47Z","timestamp":1679104967000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Diachronic profile of startup companies through social media"],"prefix":"10.1007","volume":"13","author":[{"given":"Ana Rita","family":"Peixoto","sequence":"first","affiliation":[]},{"given":"Ana","family":"de Almeida","sequence":"additional","affiliation":[]},{"given":"Nuno","family":"Ant\u00f3nio","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"Batista","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"Ribeiro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"issue":"1","key":"1055_CR1","doi-asserted-by":"publisher","first-page":"012100","DOI":"10.1088\/1742-6596\/1660\/1\/012100","volume":"1660","author":"HM Alash","year":"2020","unstructured":"Alash HM, Al-Sultany GA (2020) Improve topic modeling algorithms based on twitter hashtags. 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