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Composing tweets to increase retweets

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  • Jalali, Nima Y.
  • Papatla, Purushottam

Abstract

Brands tweet not only to communicate with followers but also to reach large audiences rapidly when the tweets are retweeted by the followers. People however will retweet only if they recognize within a few seconds that the tweet is on an interesting topic. Brands therefore need insights into how to compose tweets to facilitate topic recognition even when they are just scanned. This is the issue that we address in this research. Specifically, drawing on findings in psycholinguistics, we empirically investigate if tweets composed such that they include more topic-related words that are located closer to the start get more retweets. Results from an investigation of sales-promotional tweets by sixty brands in four categories indicate that tweets that are composed as above do get more retweets. We repeat the investigation using tweets on several other topics from a natural experiment that generated pairs of tweets where each pair is on the same topic but each tweet in the pair is composed differently. This investigation reconfirms the findings from the analysis of retweets of sales-promotional tweets. We conclude by presenting an approach for how social media managers can compose tweets based on our findings.

Suggested Citation

  • Jalali, Nima Y. & Papatla, Purushottam, 2019. "Composing tweets to increase retweets," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 647-668.
  • Handle: RePEc:eee:ijrema:v:36:y:2019:i:4:p:647-668
    DOI: 10.1016/j.ijresmar.2019.05.001
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    References listed on IDEAS

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    Cited by:

    1. Suvodeep Mazumdar & Dhavalkumar Thakker, 2020. "Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks," Future Internet, MDPI, vol. 12(12), pages 1-22, November.
    2. Hamby, Anne & Kim, Hongmin & Spezzano, Francesca, 2024. "Sensational stories: The role of narrative characteristics in distinguishing real and fake news and predicting their spread," Journal of Business Research, Elsevier, vol. 170(C).
    3. Abouk, Rahi & Jalali, Nima & Papatla, Purushottam, 2024. "Can tweets be word of mouth that changes risky behaviors?," Journal of Business Research, Elsevier, vol. 174(C).
    4. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    5. Grewal, Dhruv & Herhausen, Dennis & Ludwig, Stephan & Villarroel Ordenes, Francisco, 2022. "The Future of Digital Communication Research: Considering Dynamics and Multimodality," Journal of Retailing, Elsevier, vol. 98(2), pages 224-240.
    6. Gerrath, Maximilian H.E.E. & Mafael, Alexander & Ulqinaku, Aulona & Biraglia, Alessandro, 2023. "Service failures in times of crisis: An analysis of eWOM emotionality," Journal of Business Research, Elsevier, vol. 154(C).
    7. Oliveira, João S. & Ifie, Kemefasu & Sykora, Martin & Tsougkou, Eleni & Castro, Vitor & Elayan, Suzanne, 2022. "The effect of emotional positivity of brand-generated social media messages on consumer attention and information sharing," Journal of Business Research, Elsevier, vol. 140(C), pages 49-61.
    8. Prithwiraj Mukherjee & Souvik Dutta & Arnaud De Bruyn, 2022. "Did clickbait crack the code on virality?," Journal of the Academy of Marketing Science, Springer, vol. 50(3), pages 482-502, May.

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