Abstract
Text data constitutes a significant part of all data generated on the Internet, including the social network users’ comments and posts. Each website offers its users different functionalities. LinkedIn mainly focuses on the labor market as well as professional and business contacts, and Facebook offers the possibility of creating groups as well as photo and message sharing with friends, while Twitter allows short text message posting and tracking. One type of information researchers would like to obtain about the users of these portals is their age. Such information is crucial from the perspective of marketing, social and economic research. Each of the social networks, however, has different rules regarding the privacy policy and the publishing of information about the date of birth. This poses a problem for the researchers who would like to obtain such information. The aim of the research presented is to attempt characterization of the words typically used in the messages published by Twitter users. This social networking site was chosen due to the possibility of downloading data without additional user consent. Text mining methods and techniques were used to carry out the research, which was mainly focused on the analysis of individual words and collocations occurring in the users’ tweets.
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Notes
- 1.
The term “Silent Generation” first appeared on Novemebr 5, 1951 in the “Time” magazine in the article The younger generation.
- 2.
X is the number of years that has been declared by the users.
- 3.
The difference in the number of users results from the interval between the tweet download and the metadata. During that time, the usernames could be changed, user accounts could be deleted or blocked, which resulted in the smaller number of users in the database.
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Majkowska, A., Migdał-Najman, K., Najman, K., Raca, K. (2021). Identification of the Words Most Frequently Used by Different Generations of Twitter Users. In: Jajuga, K., Najman, K., Walesiak, M. (eds) Data Analysis and Classification. SKAD 2020. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-75190-6_3
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