Using diffusion of innovation theory and sentiment analysis to analyze attitudes toward driving adoption by Saudi women. (May 2021)
- Record Type:
- Journal Article
- Title:
- Using diffusion of innovation theory and sentiment analysis to analyze attitudes toward driving adoption by Saudi women. (May 2021)
- Main Title:
- Using diffusion of innovation theory and sentiment analysis to analyze attitudes toward driving adoption by Saudi women
- Authors:
- Al-Razgan, Muna
Alrowily, Asma
Al-Matham, Rawan N.
Alghamdi, Khulood M.
Shaabi, Maha
Alssum, Lama - Abstract:
- Abstract: Although several studies have used sentiment analysis to examine social media content, relatively few have complemented this work with sociological theories. This study employed the diffusion of innovation (DOI) framework to provide a deeper understanding of the recent debate on whether women in Saudi Arabia should be granted the right to drive. The outlook of proponents and opponents was considered by using detailed Arabic Twitter data. The sentiment analysis approach was used. The findings were analyzed on the basis of DOI stages, and the innovation–decision process demonstrated that 60% of Twitter users supported the governments' approval of women's right to drive and 40% either opposed the order or had a neutral opinion. The finding of our analysis suggests that Saudi society corresponds the DOI stages and exhibits the tendency to support the right of women to drive. This study contributes to DOI research, particularly concerning the use of social media for studying opinions on important unsettled social matters. Highlights: Applying Computational social science of Twitter data and Diffusion of Innovation theory (DOI). Conducting sentiment analysis approach on Twitter data with regards to women driving. Finding showed 60% of Twitter users supported the government approval of allowing women to drive, while 40% either opposed or had a neutral opinion. Findings suggested that Saudi society corresponds to the DOI stages and tend to support women's freedom to drive.Abstract: Although several studies have used sentiment analysis to examine social media content, relatively few have complemented this work with sociological theories. This study employed the diffusion of innovation (DOI) framework to provide a deeper understanding of the recent debate on whether women in Saudi Arabia should be granted the right to drive. The outlook of proponents and opponents was considered by using detailed Arabic Twitter data. The sentiment analysis approach was used. The findings were analyzed on the basis of DOI stages, and the innovation–decision process demonstrated that 60% of Twitter users supported the governments' approval of women's right to drive and 40% either opposed the order or had a neutral opinion. The finding of our analysis suggests that Saudi society corresponds the DOI stages and exhibits the tendency to support the right of women to drive. This study contributes to DOI research, particularly concerning the use of social media for studying opinions on important unsettled social matters. Highlights: Applying Computational social science of Twitter data and Diffusion of Innovation theory (DOI). Conducting sentiment analysis approach on Twitter data with regards to women driving. Finding showed 60% of Twitter users supported the government approval of allowing women to drive, while 40% either opposed or had a neutral opinion. Findings suggested that Saudi society corresponds to the DOI stages and tend to support women's freedom to drive. The article advances research relevant to Social media studies, sociology studies, and cultural studies. … (more)
- Is Part Of:
- Technology in society. Volume 65(2021)
- Journal:
- Technology in society
- Issue:
- Volume 65(2021)
- Issue Display:
- Volume 65, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 65
- Issue:
- 2021
- Issue Sort Value:
- 2021-0065-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-05
- Subjects:
- Computational social sciences -- Diffusion of innovation -- Saudi Arabia -- Sentiment analysis -- Twitter -- Social media
ARRF Attribute-Relation File Format -- CSS Computational social science -- DOI Diffusion of innovation -- ICT Information and communications technology -- MSA Modern Standard Arabic -- SVM Support vector machine
Technology -- Social aspects -- Periodicals
303.483 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0160791X/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.techsoc.2021.101558 ↗
- Languages:
- English
- ISSNs:
- 0160-791X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8761.023000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16824.xml