Promoting Social Change – Assessing How Twitter Was Used to Reduce Drunk Driving Behaviours Over New Year’s Eve

Shaw, Alan B.

doi:10.1080/10496491.2020.1838025

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…The specific JavaScript libraries used by this research tool include -jquery-ui.min.js [106], jquery.tinysort.min.js [107], masonry.pkgd.min.js [108], d3.layout.cloud.js [109], d3.min.js [110], analytics.js [111], and loader.js [112]. This is a popular tool amongst academic researchers in this field and has been used for several interdisciplinary applications that focused on studying and analysis of Tweets in the last few years [113][114][115][116][117][118][119][120][121]. These include -analysis of the literacy of the Twitter metaverse [113], preparing Twitter data for a MANCOVA Test [114], studying the usage of Twitter to reduce drunk driving on New Year's Eve [115], tracking fake news related to COVID-19 vaccinations [116], studying polarized politics communicated via Twitter in India [117], assessing social support and stress in autism-focused virtual communities on Twitter [118], tracking online propaganda by Twitter usage [119], investigating the proliferation of Twitter accounts in a Higher Education setting [120], and studying market identity claims on Twitter [121].…”

Section: Overview Of Social Bearingmentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur

Han

2022

COVID

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This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12,028 tweets about the Omicron variant were studied, and the specific characteristics of the tweets that were analyzed include sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a ‘neutral’ emotion. The other emotions—‘bad’, ‘good’, ‘terrible’, and ‘great’—were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other domains. Finally, to support research and development in this field, we have developed an open-access Twitter dataset that comprises Tweet IDs of more than 500,000 tweets about the Omicron variant, posted on Twitter since the first detected case of this variant on 24 November 2021.

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Section: Overview Of Social Bearingmentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur

Han

2022

COVID

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“…The specific JavaScript libraries used by this research tool include: jquery-ui.min.js [106], jquery.tinysort.min.js [107], masonry.pkgd.min.js [108], d3.layout.cloud.js [109], d3.min.js [110], analytics.js [111], and loader.js [112]. This is a popular tool amongst academic researchers in this field and has been used for several interdisciplinary applications that focused on the studying and analysis of tweets in the last few years [113][114][115][116][117][118][119][120][121]; these include the analysis of the literacy of the Twitter metaverse [113], preparing Twitter data for a Multivariate Analysis of Covariance (MANCOVA) Test [114], studying the usage of Twitter to reduce drunk driving on New Year's Eve [115], tracking fake news related to COVID-19 vaccinations [116], studying polarized politics communicated via Twitter in India [117], assessing social support and stress in autism-focused virtual communities on Twitter [118], tracking online propaganda by Twitter usage [119], investigating the proliferation of Twitter accounts in a Higher Education setting [120], and studying market identity claims on Twitter [121].…”

Section: Overview Of Social Bearingmentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur¹,

Han²

2022

Preprint

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This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and &lt;2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and &lt;1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Finally, in terms of embedded URL analysis, the most common URLs embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs.

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“…The popularity of social networks and new technologies should also be used to improve traffic safety ( 25 ). In his study, Shaw examines the impact of Twitter on reducing driving under the influence of alcohol during New Year’s Eve ( 26 ). The study identified that social marketing organizations were poor at exploiting Twitter, but the general public was prolific in disseminating anti-drunk driving behaviors.…”

Section: Literature Reviewmentioning

confidence: 99%

What Affects the Perception of a Drunk Driving Campaign?

Pešić

Trifunović

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Driving under the influence of alcohol has been recognized as a significant contributing factor to the occurrence of one out of four road traffic crashes. Young drivers are acknowledged as a particularly hazardous group of road users because of insufficient driving experience, but also because of unsafe behavior while behind the wheel, which encompasses driving under the influence of alcohol. The primary aim of this study was to examine the perception of a campaign about driving under the influence of alcohol, while the secondary goal is to present the self-reported behavior and attitudes of young drivers about driving under the influence of alcohol in the Republic of Serbia. Data were collected through a questionnaire created by a multidisciplinary team of experts in the fields of traffic engineering, psychology, and education. A total of 4,015 respondents participated in the study, aged from 16 to 30. The results show that younger respondents have a 1.89 times higher chance of remembering the road traffic safety campaign then older ones. In addition, respondents were 5.057 times more likely to perceive and remember campaign messages that have fewer words than campaign messages that have more words.

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Promoting Social Change – Assessing How Twitter Was Used to Reduce Drunk Driving Behaviours Over New Year’s Eve

Cited by 4 publications

References 55 publications

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

What Affects the Perception of a Drunk Driving Campaign?

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