A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines

Kutela, Boniphace; Magehema, Raynard Tom; Langa, Neema; Steven, Felistus; Mwekh'iga, Rafael John

doi:10.1016/j.jjimei.2022.100123

Cited by 13 publications

(1 citation statement)

References 29 publications

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“…Several previous studies have used social media data (Ghandour et al 2020;Ghandour, Hammoud, and Telesca 2019;Gu, Qian, and Chen 2016;Kutela, Magehema, et al 2022;Zhang et al 2018). For instance, Zhang et al (2018) applied deep learning to detect traffic accidents from social media data in two metropolitan areas: Northern Virginia and New York City.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Social Media Data to Understand Spatial and Severity of Roadway Crashes in Tanzania

Kutela

Mwekh’iga²,

Kilaini³

et al. 2022

JSS

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Traffic crashes are among the major cause of fatalities in most developing countries. A lack of a centralized historical crash database impedes the efforts to perform large-scale studies to understand the causes of crashes. However, the information for most major crashes are normally shared on social media. Therefore, this study used social media posts to extract information related to head-on and run-off roadway (ROR) crashes that occurred between 2010 and 2020. A total of 661 crashes were collected, which included 409 head-on and 252 ROR crashes. Geographical Information System (GIS) applications and multinomial logit models were developed to understand the spatial distribution of crashes and the resulting severity. It was found that the spatial distribution of crashes differs significantly by type and severity. Head-on collisions were predominant in two eastern regions, while ROR collisions appeared to spread on a large scale across the entire country. The pattern of fatal collision includes one southern region in addition to the two Eastern regions. Conversely, the pattern of non-injury crashes shows predominance along the coastal area. The Multinomial model results showed that speeding was two times more likely to result in fatalities for ROR crashes. Crashes in rural areas were about two times more likely to be fatal, given that they were head-on collisions. Further, bus-involved crashes were less likely to result in injuries. The implications of these study findings to the practitioners are discussed.

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