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Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

Author

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  • Ioannis Politis

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Georgios Georgiadis

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Aristomenis Kopsacheilis

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Anastasia Nikolaidou

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Panagiotis Papaioannou

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.

Suggested Citation

  • Ioannis Politis & Georgios Georgiadis & Aristomenis Kopsacheilis & Anastasia Nikolaidou & Panagiotis Papaioannou, 2021. "Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13356-:d:693599
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    References listed on IDEAS

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    1. Tiziana Campisi & Socrates Basbas & Anastasios Skoufas & Nurten Akgün & Dario Ticali & Giovanni Tesoriere, 2020. "The Impact of COVID-19 Pandemic on the Resilience of Sustainable Mobility in Sicily," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
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    3. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
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    2. Yanlong Guo & Lan Zu & Denghang Chen & Han Zhang, 2023. "A Study of Public Attitudes toward Shanghai’s Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo," IJERPH, MDPI, vol. 20(3), pages 1-27, January.

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