Rules of engagement: Practice what you tweet

Wigley, Shelley; Lewis, Bobbi Kay

doi:10.1016/j.pubrev.2011.08.020

Cited by 42 publications

(19 citation statements)

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“…This agrees with a previous study [24] indicating that replies were associated with greater message diffusion. Our results disagree with others indicating that replies address specific users, not the whole Twitter network so that those not involved might be disengaged [25].…”

Section: Discussionsupporting

confidence: 93%

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

et al. 2018

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BackgroundIncreasing the reach of messages disseminated through Twitter promotes the success of Twitter-based health education campaigns.ObjectiveThis study aimed to identify factors associated with reach in a dental Twitter network (1) initially and (2) sustainably at individual and network levels.MethodsWe used instructors’ and students’ Twitter usernames from a Saudi dental school in 2016-2017 and applied Gephi (a social network analysis tool) and social media analytics to calculate user and network metrics. Content analysis was performed to identify users disseminating oral health information. The study outcomes were reach at baseline and sustainably over 1.5 years. The explanatory variables were indicators of popularity (number of followers, likes, tweets retweeted by others), communication pattern (number of tweets, retweets, replies, tweeting/ retweeting oral health information or not). Multiple logistic regression models were used to investigate associations.ResultsAmong dental users, 31.8% had reach at baseline and 62.9% at the end of the study, reaching a total of 749,923 and dropping to 37,169 users at the end. At an individual level, reach was associated with the number of followers (baseline: odds ratio, OR=1.003, 95% CI=1.001-1.005 and sustainability: OR=1.002, 95% CI=1.0001-1.003), likes (baseline: OR=1.001, 95% CI=1.0001-1.002 and sustainability: OR=1.0031, 95% CI=1.0003-1.002), and replies (baseline: OR=1.02, 95% CI=1.005-1.04 and sustainability: OR=1.02, 95% CI=1.004-1.03). At the network level, users with the least followers, tweets, retweets, and replies had the greatest reach.ConclusionsReach was reduced by time. Factors increasing reach at the user level had different impact at the network level. More than one strategy is needed to maximize reach.

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Section: Discussionsupporting

confidence: 93%

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

et al. 2018

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“…But as with replies, these social factors are linked to user popularity (Burton and Soboleva 2011;Wigley and Lewis 2012). This contradicts the advice of studies into business engagement, which finds significant positive effects of interaction on message diffusion (Zhang, Jansen, and Chowdhury 2011;Wigley and Lewis 2012). the medium.…”

Section: Message Characteristicsmentioning

confidence: 78%

Police message diffusion on Twitter: analysing the reach of social media communications

Velde

Meijer

Homburg

2014

Behaviour & Information Technology

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Social media are becoming increasingly important for communication between government organisations and citizens. Although research on this issue is expanding, the structure of these new communication patterns is still poorly understood. This study contributes to our understanding of these new communication patterns by developing an explanatory model of message diffusion on social media. Messages from 964 Dutch police force Twitter accounts are analysed using trace data drawn from the Twitter™ API to explain why certain police tweets are forwarded and others are not. Based on an iterative human calibration procedure, message topics were automatically coded based on customised lexicons. A principal component analysis of message characteristics generated four distinct patterns of use in (in)personal communication and new/versus reproduced content. Message characteristics were combined with user characteristics in a multilevel logistic general linear model. Our main results show that URLs or use of informal communication increases chances of message forwarding. In addition, contextual factors such as user characteristics impact diffusion probability. Recommendations are discussed for further research into authorship styles and their implications for social media message diffusion. For the police and other government practitioners, a list of recommendation about how to reach a larger number of citizens through social media communications is presented.

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“…The use of webcare teams may be of use here. Previous research has shown that these customer-centered teams who aim to resolve problems contribute to more positive company evaluations (Van Noort & Willemsen, 2011) and eventually result in fewer negative messages online (Wigley & Lewis, 2011). Given the implications of this research, we encourage such webcare teams to develop and/or use mechanisms to detect disclosure of negative emotions as soon as possible, as well as mechanisms to engage in online conversations with consumers who recently expressed themselves negatively.…”

Section: Practical Implicationsmentioning

confidence: 93%

Negative online word-of-mouth: Behavioral indicator or emotional release?

Verhagen

Nauta

Feldberg

2013

Computers in Human Behavior

175

125

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The influence of negative online word-of-mouth on the behavior of those receiving it has been addressed extensively in the academic literature. Remarkably, the question whether negative online word-of-mouth should also be seen as a behavioral indicator of its sender remains unaddressed. Answering this question is relevant as it provides companies with insight into the need to engage in interaction with those who negatively express themselves online or whether these expressions should be seen as temporary emotional releases without any future conduct. To fill the existing research gap, this research paper proposes and empirically tests a sender-oriented model, investigating the influence of emotions, negative online word-of-mouth on repatronage and switching behavior. As disclosing negative feedback online may also reflect the sender's motivation to inform the consumer community or to provide constructive feedback to the company responsible for the dissatisfying consumption, community usefulness and company usefulness are included as behavioral moderators. The results of an empirical survey conducted amongst real senders of negative information confirm that negative online word-of-mouth is directly driven by positive and negative emotions and is strongly predictive for the sender's future conduct. The motivation to help other consumers was demonstrated to function as behavioral moderator. The paper concludes with theoretical and managerial implications, and suggests avenues for further research.

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Rules of engagement: Practice what you tweet

Cited by 42 publications

References 1 publication

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

Police message diffusion on Twitter: analysing the reach of social media communications

Negative online word-of-mouth: Behavioral indicator or emotional release?

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