Questionnaire Testing: Identifying Twitter User's Information Sharing Behavior during Disasters

Abdullah, Nurhayati; Nishioka, Dai; Murayama, Yuko

doi:10.2197/ipsjjip.24.20

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Recent work has also applied retweeting motivations in specific contexts. For example, during disaster situations, users usually retweet to share information, to convey the significance of information in their network, to express their feelings, or to get feedback/alert other people[ 22 ]. A conceptualization and assessment of retweeting motivations can offer valuable extensions to the current literature.…”

Section: Introductionmentioning

confidence: 99%

The Why We Retweet scale

et al. 2018

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BackgroundTwitter offers a platform for rapid diffusion of information and its users' attitudes and behaviors. Insights about information propagation via retweets (the message forwarding function) offer observable explanations of ways in which modern human interactions get organized in the form of online networks, and contextualized in the form of public health, policy decisions, disaster management, and civic participation. This study conceptualized and validated the Why We Retweet Scale to contextualize retweeting behavior.ObjectiveTwitter users were identified using clustering algorithms that consider a users’ position in their network and invited for an online survey. Participants (N = 1433) responded to 19 questions about why they retweet. Exploratory factor Analysis (EFA) was conducted on a scale development sample (70% of original sample), which informed the Confirmatory Factor Analysis (CFA) on a scale testing sample (30% of the original sample). Varimax rotation was used to obtain a rotated factor solution, which resulted in interpretable factors. Demographic differences among scale factors were analyzed using one-way ANOVA or independent samples t-tests.ResultsThe final model (χ221 = 28, RMSEA = .03 [90% CI, 0.00–0.06], CFA = .99, TLI = 0.99) represented a parsimonious solution with 4 factors, measured by 2–3 items each, creating a final scale consisting of 9 items. Factor labels and definitions were: (1) Show approval, “Show support to the tweeter”; (2) Argue, “To argue against a tweet that I disagree with”; (3) Gain attention, “Add followers or gain attention”; and (4) Entertain, “Create humor/amusement”. Demographic differences were also reported.ConclusionsThe Why We Retweet Scale offers a useful conceptualization and assessment of motivations for retweeting. In the future, communication strategists might consider the factors associated with information propagation when designing campaign messages to maximize message reach and engagement on Twitter.

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Section: Introductionmentioning

confidence: 99%

The Why We Retweet scale

et al. 2018

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“…This dataset contains 333 URLs, accounting for 0.73% of the unique URLs. Previous studies have shown, the use of the retweet count (used in the ‘most retweeted tweets’ dataset) can be an indication of tweets that the most users express: the significance of information to their followers, feelings and/or get feedback/alert others [ 68 , 69 ]. Retweets are used rather than likes in this paper as the focus is on the circulation of information, hence they are an important aspect of the social media information ecology [ 11 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

Information sharing practices during the COVID-19 pandemic: A case study about face masks

Baker

Concannon

2022

PLoS ONE

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This article contributes an empirical analysis of information sharing practices on Twitter relating to the use of face masks in the context of COVID-19. Behavioural changes, such as the use of face masks, are often influenced by people’s knowledge and perceptions, which in turn can be affected by the information available to them. Face masks were not recommended for use by the UK public at the beginning of the COVID-19 pandemic. Due to developments in scientific understanding, the guidance changed and by the end of 2020 they were mandatory on public transport and in shops. This research examines tweets in this longitudinal context and, therefore, provides novel insights into the dynamics of crisis communication in an ongoing crisis event with emerging scientific evidence. Specifically, analysis of the content of tweets, external resources most frequently shared, and users sharing information are considered. The conclusions contribute to developing understanding of the digital information ecology and provide practical insights for crisis communicators. Firstly, the analysis shows changes in the frequency of tweets about the topic correspond with key guidance and policy changes. These are, therefore, points in time official channels of information need to utilise the public’s information seeking and sharing practices. Secondly, due to changes in face mask guidance and policy, the current literature on digital information ecology is insufficient for capturing the dynamic nature of a long-term ongoing crisis event. Challenges can arise due to the prolonged circulation of out-of-date information, i.e. not strategic misinformation, nor “mis”-information at all, which can have serious ramifications for crisis communication practitioners. Thirdly, the role of traditional media and other journalism/broadcasting platforms in shaping conversations is evident, as is the potential for scientific organisations’ and individual people’s Twitter user accounts. This plurality of contributors needs to be acknowledged and understood to inform crisis communication strategies.

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“…Data from the second stage is then used as input in the third activity of P3, namely PLS-SEM analysis which produces statistical information about the test indicators, reliability and validity [45][46][47][48][49]. Stage P4 conducts interpretative analysis activities that produce important justifications related to testing the questionnaire [50][51][52]. Stage P5 is a questionnaire renewal activity to get a better questionnaire than the previous questionnaire [53].…”

Section: Figure 2 Stages Of Researchmentioning

confidence: 99%