Abstract

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> Twitter offers tools that facilitate the diffusion of information by which companies can engage consumers to share their messages. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> Communication professionals are using platforms such as Twitter to disseminate information; however, the strategies that they should use to achieve high information diffusion are not clear. This article proposes message repetition as a strategy. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</b> 1. What is the wear-out point of Twitter? 2. How many times should a company repeat a tweet written on its brand page to maximize the diffusion for seeds? 3. How many times should a company repeat a tweet written on its brand page to maximize the diffusion while minimizing the number of consumers reaching their wear-out point for seeds? 4. How many times should a company repeat a tweet written on its brand page to maximize the diffusion for nonseeds? 5. How many times should a company repeat a tweet written on its brand page to maximize the diffusion while minimizing the number of consumers reaching their wear-out point for both seeds and nonseeds? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research methodology:</b> An agent-based simulation model for information diffusion is proposed as an approach to measure the diffusion of a tweet that has been repeated. The model considers that consumers can reach their wear-out point when they read a tweet several times. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> The results of the model indicate the number of times a company should send the same tweet to achieve high information diffusion before this action has negative effects on consumers. Brand followers are key to achieving high information diffusion; however, consumers begin to feel bothered by the tweet by the sixth repetition. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</b> To the best of our knowledge, this is the first study to examine tweet repetition as a strategy to achieve higher information diffusion on Twitter. In addition, it extends the information diffusion literature by controlling the wear-out effect. It contributes to both communication and computational science literature by analyzing a communication problem using an agent-based approach. Finally, this article contributes to the field of technical and professional communication by testing a strategy to reach great information diffusion, and by creating a tool that any company can use to anticipate the results of a communication campaign created in Twitter before launching it.

Journal
IEEE Transactions on Professional Communication
Published
2023-06-01
DOI
10.1109/tpc.2023.3260449
CompPile
Search in CompPile ↗
Open Access
Closed
Topics
Export

Citation Context

Cited by in this index (1)

  1. IEEE Transactions on Professional Communication

Cites in this index (4)

  1. IEEE Transactions on Professional Communication
  2. IEEE Transactions on Professional Communication
  3. IEEE Transactions on Professional Communication
  4. Technical Communication Quarterly
Also cites 57 works outside this index ↓
  1. 10.1080/01633392.1988.10504936
  2. 10.4324/9781315806099
  3. 10.1086/376800
  4. 10.4135/9781452229379
  5. 10.1080/00913367.2015.1018460
  6. 10.3758/BF03212593
  7. 10.1007/s11747-014-0414-5
  8. 10.1016/j.intmar.2012.01.003
  9. 10.1037/h0076165
  10. 10.2753/MIS0742-1222290408
  11. 10.1080/1369118X.2013.853817
  12. 10.1080/02650487.2015.1037708
  13. 10.1080/15252019.2015.1016636
  14. 10.1016/j.tele.2021.101623
  15. 10.1016/j.jbusres.2014.11.027
  16. 10.1177/2329488415572788
  17. 10.1016/j.chb.2014.11.008
  18. 10.1109/SocialCom.2010.33
  19. 10.1609/icwsm.v4i1.14033
  20. 10.1016/j.bushor.2014.02.001
  21. 10.1109/HICSS.2010.412
  22. 10.1016/j.chb.2014.04.020
  23. 10.1007/s11573- 020-01022-9
  24. 10.31449/inf.v45i6.3575
  25. 10.1177/0165551514565318
  26. 10.1509/jm.74.2.71
  27. 10.1509/jm.10.0088
  28. 10.1108/IntR-07-2016-0223
  29. 10.1007/s11747-014-0388-3
  30. 10.1080/00913367.2013.827606
  31. 10.1007/s42979-021-00836-w
  32. 10.1145/2872518.2889393
  33. 10.1145/2487788.2488110
  34. 10.1007/s12525-011-0065-z
  35. 10.1016/j.pubrev.2010.12.001
  36. 10.1111/jcc4.12074
  37. 10.1145/2187836.2187907
  38. 10.3390/info13010013
  39. 10.4135/9781412983327
  40. 10.1007/978-3-030-36525-7_9
  41. 10.1108/IMDS-07-2016-0290
  42. 10.1080/1206212X.2020.1758430
  43. 10.1016/j.dss.2012.12.022
  44. 10.1080/02650487.2016.1173765
  45. 10.1080/10641734.1998.10505073
  46. 10.1177/0047287520951639
  47. 10.1108/EJM-03-2017-0182
  48. 10.1509/jmr.15.0443
  49. 10.1016/j.ijresmar.2011.04.002
  50. 10.1080/02650487.2016.1240469
  51. 10.2501/JAR-51-1-258-275
  52. 10.1509/jmr.11.0305
  53. 10.1023/A:1011122126881
  54. 10.1108/OIR-02-2018-0065
  55. 10.1016/j.chb.2015.01.008
  56. 10.1007/978-3-642-02774-1_16
  57. 10.1007/s11129-015-9159-9