Examining the Existence of Double Jeopardy and Negative Double Jeopardy within Twitter

Please note: Changes made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher's version if you wish to cite this paper. Purpose The theory of Double Jeopardy (DJ) is shown to hold across broad ranging geographies and physical product categories. However, there is very little research appertaining to the subject within an online environment. In particular, studies that investigate the presence of DJ and the contrasting view point to DJ, namely that of Negative Double Jeopardy (NDJ), are lacking. This study contributes to this identified research gap, and examines the presence of DJ and NDJ within a product category, utilising data from Twitter. Design/methodology/approach 354,676 tweets are scraped from Twitter and their sentiment analysed and allocated into positive, negative and no-opinion clusters using fuzzy c-means clustering. The sentiment is then compared to the market share of brands within the beer product category to establish whether a DJ or NDJ effect is present. Findings The data reveals an NDJ effect with regards to original tweets (i.e. tweets which have not been retweeted). That is, when analysing tweets relating to brands within a defined beer category, we find that larger brands suffer by having an increased negativity amongst the larger proportion of tweets associated with them. Research limitations/implications The clustering approach to analyse sentiment in Twitter data brings a new direction to analysis of such sentiment. Future consideration of different numbers of clusters may further the insights this form of analysis can bring to the DJ/NDJ phenomenon. Managerial implications discuss the uncovered practitioner's paradox of NDJ and strategies for dealing with DJ and NDJ effects. Originality/value This study is the first to explore the presence of DJ and NDJ through the utilisation of sentiment analysis derived data and fuzzy clustering. DJ and NDJ are under-explored constructs in the online environment. Typically, past research examines DJ and NDJ in separate and detached fashions. Thus, the study is of theoretical value because it outlines boundaries to the DJ and NDJ conditions. Second, this research is the first study to analyse the sentiment of consumer-authored tweets to explore DJ and NDJ effects. This study also highlights the need to separate original tweets from retweets because our data shows that jeopardy dynamics differ in these …