A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines
Business entities utilize multiple languages on social media for marketing, promotions, and communication. The impact of utilizing one language over the other on engagements has not been well explored. This study applied a regression-text mining approach to over 3000 Tanzanian-based airlines' t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096822000660 |
| _version_ | 1811321127664877568 |
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| author | Boniphace Kutela Raynard Tom Magehema Neema Langa Felistus Steven Rafael John Mwekh'iga |
| author_facet | Boniphace Kutela Raynard Tom Magehema Neema Langa Felistus Steven Rafael John Mwekh'iga |
| author_sort | Boniphace Kutela |
| collection | DOAJ |
| description | Business entities utilize multiple languages on social media for marketing, promotions, and communication. The impact of utilizing one language over the other on engagements has not been well explored. This study applied a regression-text mining approach to over 3000 Tanzanian-based airlines' tweets posted between 2018 and 2020 to explore the influence of English and Swahili languages in communication and marketing. By defining engagement as the retweets and favorites, the study found that English tweets had relatively higher engagements than Swahili tweets. However, Swahili tweets with photos and videos were likely to have more engagements than English tweets. Conversely, hashtags attracted higher engagements for English tweets. Time of the day revealed mixed findings. Further, several key patterns were observed when favorites and retweets were considered separately. The practical applications of the study were also discussed. It is expected that the study findings will benefit bilingual business entities on a global scale. |
| first_indexed | 2024-04-13T13:11:25Z |
| format | Article |
| id | doaj.art-04c0d24e5f7c4a68b94733b8c6d87098 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-13T13:11:25Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-04c0d24e5f7c4a68b94733b8c6d870982022-12-22T02:45:35ZengElsevierInternational Journal of Information Management Data Insights2667-09682022-11-0122100123A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlinesBoniphace Kutela0Raynard Tom Magehema1Neema Langa2Felistus Steven3Rafael John Mwekh'iga4Roadway Safety Program, Texas A&M Transportation Institute, 1111 RELLIS Parkway, Bryan, TX 77807, United States; Corresponding author at: Roadway Safety Program, Texas A&M Transportation Institute, United States.Department of Civil Engineering, Ardhi University, P.O. Box 35176, Dar es salaam, TanzaniaDepartment of Sociology/African American Studies, University of Houston, 3551 Cullen Boulevard, Houston, TX 77204, United StatesArchbishop Mihayo University College of Tabora, P.O. Box 801, Tabora, TanzaniaDepartment of Civil Engineering, Ardhi University, P.O. Box 35176, Dar es salaam, TanzaniaBusiness entities utilize multiple languages on social media for marketing, promotions, and communication. The impact of utilizing one language over the other on engagements has not been well explored. This study applied a regression-text mining approach to over 3000 Tanzanian-based airlines' tweets posted between 2018 and 2020 to explore the influence of English and Swahili languages in communication and marketing. By defining engagement as the retweets and favorites, the study found that English tweets had relatively higher engagements than Swahili tweets. However, Swahili tweets with photos and videos were likely to have more engagements than English tweets. Conversely, hashtags attracted higher engagements for English tweets. Time of the day revealed mixed findings. Further, several key patterns were observed when favorites and retweets were considered separately. The practical applications of the study were also discussed. It is expected that the study findings will benefit bilingual business entities on a global scale.http://www.sciencedirect.com/science/article/pii/S2667096822000660Multiple languagesSocial media engagementAirline marketingText-mining |
| spellingShingle | Boniphace Kutela Raynard Tom Magehema Neema Langa Felistus Steven Rafael John Mwekh'iga A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines International Journal of Information Management Data Insights Multiple languages Social media engagement Airline marketing Text-mining |
| title | A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines |
| title_full | A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines |
| title_fullStr | A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines |
| title_full_unstemmed | A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines |
| title_short | A comparative analysis of followers' engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines |
| title_sort | comparative analysis of followers engagements on bilingual tweets using regression text mining approach a case of tanzanian based airlines |
| topic | Multiple languages Social media engagement Airline marketing Text-mining |
| url | http://www.sciencedirect.com/science/article/pii/S2667096822000660 |
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