‘The Tireless Selling-Machine’ – Commercial Deployment of Social Bots during Black Friday Season on Twitter

Bibtex

Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "‘The Tireless Selling-Machine’ – Commercial Deployment of Social Bots during Black Friday Season on Twitter", 
							Author= "Felix Brünker, Julian Marx, Björn Ross, Milad Mirbabaie, and Stefan Stieglitz", 
							Doi= "https://doi.org/10.30844/wi_2020_n6-bruenker", 
							 Abstract= "In recent years, mainstream E-commerce platforms have changed the way consumer goods are merchandized online. While such platforms increasingly discover social elements to drive sales, social media start implementing E-commerce features themselves. This emerging intersection also provides space for bot activity in a Social Commerce (SC) context, which is sparsely understood. In this short paper, we investigate the deployment of social bots during a large-scale commercial event. To this end, we applied bot detection techniques to the Twitter communication during the 2018 Black Friday season. Using three distinct metrics, we identified 42 bot-like accounts. A manual classification of 11,889 tweets found those bots to be primarily deployed in pre-transactional phases of SC to promote third party products and to initiate external transactions. Our results further suggest adapting detection metrics to event-specific user behavior. Further research aims at the dynamics, impact, and network positions of SC bots.

", 
							 Keywords= "Social Bots, Social Commerce, Social Media, E-Commerce
", 
							}
					
Felix Brünker, Julian Marx, Björn Ross, Milad Mirbabaie, and Stefan Stieglitz: ‘The Tireless Selling-Machine’ – Commercial Deployment of Social Bots during Black Friday Season on Twitter. Online: https://doi.org/10.30844/wi_2020_n6-bruenker (Abgerufen 23.05.24)

Abstract

Abstract

In recent years, mainstream E-commerce platforms have changed the way consumer goods are merchandized online. While such platforms increasingly discover social elements to drive sales, social media start implementing E-commerce features themselves. This emerging intersection also provides space for bot activity in a Social Commerce (SC) context, which is sparsely understood. In this short paper, we investigate the deployment of social bots during a large-scale commercial event. To this end, we applied bot detection techniques to the Twitter communication during the 2018 Black Friday season. Using three distinct metrics, we identified 42 bot-like accounts. A manual classification of 11,889 tweets found those bots to be primarily deployed in pre-transactional phases of SC to promote third party products and to initiate external transactions. Our results further suggest adapting detection metrics to event-specific user behavior. Further research aims at the dynamics, impact, and network positions of SC bots.

Keywords

Schlüsselwörter

Social Bots, Social Commerce, Social Media, E-Commerce

References

Referenzen

1. Zhou, W., Duan, W.: The Sales Impact of Word-of-Mouth Distribution across Retail and Third-Party Websites. In: Proceedings of the Thirty Seventh International Conference on Information Systems, Dublin, Ireland (2016).
2. Teubner, T., Hawlitschek, F., Adam, M.T.P.: Reputation Transfer. Bus. Inf. Syst. Eng. 61, 229–235 (2019).
3. Van den Broeck, E., Zarouali, B., Poels, K.: Chatbot advertising effectiveness: When does the message get through? Comput. Human Behav. 98, 150–157 (2019).
4. Yang, K., Varol, O., Davis, C.A., Ferrara, E., Flammini, A., Menczer, F.: Arming the public with artificial intelligence to counter social bots. Hum. Behav. Emerg. Technol. 1, 48–61 (2019).
5. Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The Rise of Social Bots. Commun. ACM. 59, 96–104 (2014).
6. Brachten, F., Stieglitz, S., Hofeditz, L., Kloppenborg, K., Reimann, A.: Strategies and Influence of Social Bots in a 2017 German state election – A case study on Twitter. In: Australasian Conference on Information Systems (2017).
7. Bessi, A., Ferrara, E.: Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21 (2016).
8. Brachten, F., Mirbabaie, M., Stieglitz, S., Berger, O., Bludau, S., Schrickel, K.: Threat or Opportunity? – Examining Social Bots in Social Media Crisis Communication. In: Australasian Conference on Information Systems, Sydney, Australia (2018).
9. Farivar, S., Yuan, Y., Turel, O.: Understanding Social Commerce Acceptance: The Role of Trust, Perceived Risk, and Benefit. In: Twentysecond Americas Conference on Information Systems, San Diego, USA (2016).
10. Porturak, M., Softic, S.: Influence of Social Media Content on Consumer Purchase Intention: Mediation Effect of Brand Equity. Eurasian J. Bus. Econ. 12, 17–43 (2019).
11. Liang, T.-P., Ho, Y.-T., Li, Y.-W., Turban, E.: What Drives Social Commerce: The Role of Social Support and Relationship Quality. Int. J. Electron. Commer. 16, 69–90 (2011).
12. Saundage, D., Lee, C.Y.: Social Commerce Activities – a taxonomy. In: Australasian Conference on Information Systems, Sydney, Australia (2011).
13. Wang, W., Chen, R.R., Ou, C.X., Ren, S.J.: Media or message, which is the king in social commerce?: An empirical study of participants’ intention to repost marketing messages on social media. Comput. Human Behav. 93, 176– 191 (2019).
14. Lee, S.H., Noh, S.E., Kim, H.W.: A mixed methods approach to electronic word-of-mouth in the open-market context. Int. J. Inf. Manage. 33, 687–696 (2013).
15. Venkatesh, V., Brown, S.A., Bala, H.: Bridging the Qualitative-Quantitative Divide: Guidelines for Conducting Mixed Methods Research in Information Systems. MISQ. 37, 21–54 (2013).
16. Ross, B., Brachten, F., Stieglitz, S., Wikström, P., Moon, B., Münch, F.V., Bruns, A.: Social Bots in a Commercial Context – A Case Study on Soundcloud. In: Twenty-Sixth European Conference on Information Systems, Portsmouth, UK (2018).
17. Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: Online Human-Bot Interactions: Detection, Estimation, and Characterization. In: Proceedings of the Eleventh International AAAI Conference on Web and Social Media, Montreal, Canada (2017).
18. Liu, X.: A big data approach to examining social bots on Twitter. J. Serv. Mark. (2019).
19. Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. (Ny). 467, 312–322 (2018).
20. Mayring, P.: Qualitative Content Analysis (2014).

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