Detecting a citizens' activity profile of an urban territory through natural language processing of social media data

TIKHONOVA, Olga, Aleksandr ANTONOV, Jura BOGOMOLOV, Devashish KHULBE and Stanislav SOBOLEVSKY. Detecting a citizens' activity profile of an urban territory through natural language processing of social media data. In Boukhanovsky A., Krzhizhanovskaya V., Klimova A. Procedia Computer Science. Amsterdam: Elsevier, 2022, p. 11-22. ISSN 1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2022.10.203.

Basic information

• Original name: Detecting a citizens' activity profile of an urban territory through natural language processing of social media data
• Authors: TIKHONOVA, Olga (guarantor), Aleksandr ANTONOV, Jura BOGOMOLOV (840 United States of America, belonging to the institution), Devashish KHULBE (356 India, belonging to the institution) and Stanislav SOBOLEVSKY (112 Belarus, belonging to the institution).
• Edition: Amsterdam, Procedia Computer Science, p. 11-22, 12 pp. 2022.
• Publisher: Elsevier

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10100 1.1 Mathematics
• Country of publisher: Netherlands
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: printed version "print"
• WWW: URL
• RIV identification code: RIV/00216224:14310/22:00130436
• Organization unit: Faculty of Science
• ISSN: 1877-0509
• Doi: http://dx.doi.org/10.1016/j.procs.2022.10.203
• Keywords in English: urban data analysis; social networks; Natural Language Processing (NLP); Named Entity Recognition; Classification (NERC); Subject-Predicate-Object (SPO) triplets’ extraction
• Tags: International impact, Reviewed

Abstract

The article presents the premises, process, and outcomes of the research, devoted to investigation of the suitability of natural language processing approaches (named entity recognition and subject-predicate-object triplets’ extraction, in particular), applied to social media data, for the problem of building a profile of citizens' activity in an urban territory. Using the named entity recognition approach, supplemented with the custom method of named urban entities distillation, it was possible to build a detailed and representative list of named urban entities for the sample territory of Hatfield, Hertfordshire. Using the subject-predicate-object triplets’ extraction approach, supplemented with the custom activity description patterns, it was possible to get the picture of citizens’ activity corresponding to the identified urban entities. The outcomes were verified on the Twitter and Instagram social networks data and evaluated from the perspectives of the resulting profile quality.