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Effectiveness and downside risks of employing firm-controlled agents to calm down online firestorms
12th OUIWS, Harvard Business School, 2014
byNik Franke, Thomas Funke, Peter Keinz, and Alfred Taudes
Phenomenon and research question
In this project, we explore a strategy to calm down emerging online firestorms.
Online firestorms
“… a sudden discharge of large quantities of [...] complaint behavior against a [...] company [...] in social media networks.“ 1
Major threat to a company’s reputation2
Research questions:
1.) Can agents of protection effectively help to calm down online firestorms?
2.) Which factors influence their effectiveness?
Nik Franke: hey guys, calm down a bit. singtel is setting up the 4G network to improve their service to all of us...so they do care for us… and by the way: i‘ve never experienced any problems …i‘d recommend their service to my friends ...
Method
We used case-based agent-based modeling to investigate the RQs.
Step 1: Building the model
Step 2: Validating the model
Step 3: Running experiments
time
Simulation (1st model)
# of
pee
rs p
artic
ipati
ng
Real conflict
t1 t3
Case study to gain data about a real-life online firestorm
Variation of - number & roles agents- community characteristics
Literature review to derive a general model on opinion diffusion3
Preliminary findings & next steps
Agents of protection can significantly affect online firestorms.
No agents of protection
10 opinion leaders as
agents of protection
Robust patterns across all scenarios:
Agents of protection
- reduce intensity, speed, and duration of an online firestorm (p<.001)
- increase the average attitude towards the company after an online firestorm (p<.001)
t1
t3
t3
Next steps:
- Extending the model to account for effects triggered by un-covered agents of protection- Attempt to find “optimal” infiltration strategy
Thank you for your attention!
Literature:
1)
Pfeffer, J., T. Zorbach, and K. M. Carley. "Understanding online firestorms: Negative word-of-mouth dynamics in social media networks." Journal of Marketing Communications 20.1-2 (2014): 117-128.
2)
Stich, L., G. Golla, and A. Nanopoulos. "Modelling the spread of negative word-of-mouth in online social networks." Journal of Decision Systems 23.2 (2014): 203-221.
3)
Hegselmann, R, and U. Krause. "Opinion dynamics and bounded confidence models, analysis, and simulation." Journal of Artificial Societies and Social Simulation 5.3 (2002).
Miller, K.D., F. Fabian, and S. Lin. "Strategies for online communities." Strategic Management Journal 30.3 (2009): 305-322.