1

Building reputation vectors using honeypot profiles on FacebookNasri Messarra, Anne MioneUniversité de Montpellier 1 - MRM

2 Background E-reputation has become an important concern for firms

Pampers, Nestlé and other brands have already paid the heavy price of fan attacks (Champoux et al., 2012; Paul Gillin, 2012; Steel, 2010).

The observation of the buzz and more particularly of the negative buzz (bad buzz) is important (Cuvelier, Aufaure, 2011)

Attacks on Facebook are more frequent and research is required to better understand and counteract them

3 Litterature review 1 : E-reputation 1990 : Howard RHEINGOLD creates the concept of « e-reputation ».

He evoques a « digital social life »: public debates, controversies have an impact on the reputation of the firm, individuals, associations, etc…

2000 : appearance of tools to manage e-reputation customer ratings (evaluation of the supplier by customers on Ebay

auctions, evaluation of books by customers on Amazon, etc.) and positive and negative buzz management :

contribute to positive buzz and identify and counteract negative buzz.

4 Litterature review 1 : E-reputation E reputation)Three theoretical ambitions (François Xavier de Vaujany, Helène Lambrix, DRM)  : holistic ( the organisation is the level of analysis) Individualist ( the models are centered on individuals or a key

individual in or outside organization) Integrative ( holistic and individualists aspects are simultaneously

taken into account)Litterature concerns the shaping (distortion) of reputation: antecedents, maintenance and reputation effects (Deephouseet Carter, 2005 ; Rindova et al., 2005 ; MacMillan, 2007 ; Walsh et al., 2007 ; Lange et al., 2011)  We consider a destabilizing action on reputation

5 Litterature Review 2 : Viral Marketing

Hinz, Skiera, Barrot & Becker (2011) define 4 critical viral marketing success factors:

1. Content, in that the attractiveness of a message makes it memorable (Berger and Milman, 2011; Berger and Schwartz, 2011; Gladwell, 2002; Porter and Golan, 2006)

2. The structure of the social network (Bampo et al. 2008) 3. The behavioral characteristics of the recipients and their

incentives for sharing the message (Ardnt, 1967); 4. The seeding strategy which determines the initial set of targeted

consumers chosen by the initator of the viral campaign (Bampo et al., 2008; Kalish, Mahajan, and Muler, 1995; Libai, Muller and Peres, 2005)

6 Litterature Review 3 : Seeding strategy

There are debates in literature concerning seeding population :

- Target hubs is not sufficient (Watts et Dodds, 2007, Skiera, Barrot, Becker, 2007)- Target hubs is still important (Hinz et al. , 2011)- Size does not matter (Scarpi, 2010) and one of the keys to success is the initial seeding population (Liu-Thompkins, 2012)-Literature refers to fans and community members as “nobodies” and “somebodies” (Booth & Matic, 2011) and more and more researchers focus on the quality of the members rather than on the size of network (Scarpi, 2010; Wallace, Buil, Chernatony, 2014).

7 Research questions Can we conceive and realize an original attack of a company through

Facebook in real situation but with the General Management agreement

(Research action) ?

In order to : Check the feasability of such a strategy Increase the methodological understanding of such an attack Measure the WoM diffusion model Contribute to the seeding population strategy debate

8 Methodology

we target a small European company with around 1,500 fans on its Facebook page

European market Services activity

We create a schema

9 Methodology: How consumers act on Facebook with or against brands and companies

1- Post directly on the brand’s page

2- Post on their own social networks and wait for the viral effect of WoM to reach the brand

Reported cases & littérature about Nestlé, Pampers, DKNY, Marie-Claire, Capri Sun, Cooks Source, Bershka…

10

Personal timeline

Brand communityOn Facebook(Brand page)

A three steps attack through OSN using an optimized seeding population

1. Create a fake profile

2. Attract engaged fans of a brand to befriend the fake profile (our initial seeding population)

3. Diffuse information organically to engaged fans (no need for WOM for diffusion)

Engaged fans

Methodology: The schema

11 First step : Create a Fake profile

Based on homophily (cover experiment) Stereotype of existing fake profiles on Facebook (Barracuda networks, )

12

Cover Experiment

0 22 24 33 37 70 2090

50

100

150

200

250

29 39 4463

78

113

5075

101

131

164180

195

Cover ExperimentRequests Sent / Requests Accepted

Acceptance Request sent cumulative

Day

Frie

nds

Second step : acquiring friends

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Stereotyped fake ProfileBarracuda Networks’ statistics (Rashid, 2012): a woman living in a major city, having a high education degree, interested in both men and women…

We attract 200 engaged fansin 7 months

Second step : acquiring friends

14 Second step : acquiring friends

1. We visit the brand’s Facebook page2. We locate the fans who engaged with the brand (like, comment, share)3. We send a friend request from both profiles a week apart

15 We reuse 6 posts from the fan page of the brand

Third step : diffusion of information

16~200 engaged fans as friends

from stereotype

~100 engaged fans as friends

from coverOnly 30 mutual friends

Results : resulting network

17

In average, 105 engaged fans receive the message organically 44 “friends” engage in the conversation Generates WoM and interaction

Results: diffusion of information and generation of WoM

18 Results: Directed Communication Graph of All Conversations

Both profiles generated one larger network of communication and influence.

They acted as bridges between two sub-networks who react differently (we mentioned that our profiles only have 30 friends in common

33 engagement actions (comments/like) where made on the statuses or comments posted by one of the honeypot profiles and 44 engagement actions were made on the statuses or comments made by the other honeypot profile.

14.8% of edges (connections) are reciprocated showing that communication got back and forth between engaged fans.

The maximum geodesic distance is 5 showing that the information was viral to a certain extent and travelled from one node to the other with an average of 2.32 nodes on a path.

19 Results Before and After

  Negative posts published on the brand's Facebook

page

Same negative posts published on

fake profiles timelines using an optimized seeding

strategy

Difference

Fans engaged 11 37 +236%Engagement actions (likes, comments)

15 77 +413%

Reciprocation (back and forth communication and consumer to consumer communication)

0% 14.8% +14.8%

20 Results

Posts were directly received by engaged fans of the brand itself which shared, commented or liked these posts, generating word of mouth reaching friends of friends to the 5th level

Information was served directly to these fans at no cost (organically) and at a higher reach than it would have been possible if these posts were published directly on the company’s page.

This forced changes IRL (in real life)

21 Results This answers our initial question and confirms our hypothesis: it is possible

to attract engaged fans of a brand and distribute information organically to them bypassing the control of the brand itself and preventing it to stop the information sharing.

The campaign was a success: The company did not take any action for a while and stood as an observer until it realized that the movement was not going to stop and that it would not be able to contain it. It then tried to respond shyly to our posts and, finally, changes were made to the company’s management team.

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Back to the 4 critical viral marketing success factors (Hinz, Skiera, Barrot & Becker (2011):

1.Content (Berger and Milman, 2011; Berger and Schwartz, 2011; Gladwell, 2002; Porter and Golan, 2006)- We did not work on the content, using posts already published on the brand’s page

2. The structure of the social network (Bampo et al. 2008): We engineered a network of engaged fans of a brand around fake profiles and described a schema that can be reproduced or anticipated.

3. The behavioral characteristics of the recipients and their incentives for sharing the message (Ardnt, 1967): We helped reach more engaged fans without dispersion using organic reach (without WoM) which resulted in a higher engagement and interaction.

4. The seeding strategy (Bampo et al., 2008; Kalish, Mahajan, and Muler, 1995; Libai, Muller and Peres, 2005): We showed that an optimized initial population engages more with posts , which results in higher pressure and influence offline.

Contributions to Viral marketing, WoM Literature

23 Contributions to Social Network building methodology We engineered a network of online “friends” who may not be

friends or even know each other in real life. This social network would have never existed if it wasn’t for our experiment.

We created a new type of Honeypot profiles Webb, Caverlee and Pu (2008) defined Honeypot profiles as real profiles used to attract

fake ones. Our experiment does the opposite as it uses fake profiles to catch real ones.

attraction realized by homophily: Boshmaf, Muslukhov and Beznosov (2011) used social bots to attract friends based on

mutual frienship. Our cover experiment shows that, on Facebook, people may engage others based on homophily:

24 Contributions to e-reputation management Managers should be aware about the usage of such vectors that

could ruin the reputation of a firm.

25 Discussion We are aware that our experiments on honeypot profiles and influence

in online social networks raise ethical questions. The number of fake profile is so important nowadays that scholars have

to develop their knowledge about them, as they constitute a potential tool in influence strategies.

The same experiments can be reproduced with real Facebook accounts and the findings can be used as well to create engaged communities and improve a company’s reputation or promote brands in an ethical way.

26 Conclusion

Many scholars agree that the key to success in information diffusion is influencing the influencer (Galeotti & Goyal, 2009; Hinz et al., 2011) and using strategy to target an optimized initial seeding population (Liu-Thompkins, 2012).

Information of peers tends to be more influential than the information diffused by brands or similar source (Hinz et al., 2011).

Our contribution exposed a strategy to build an optimal seeding population from scratch in a container, the circle of friends of our fake profiles, and diffuse information organically to them.

27 Thank You

Nasri Messarra Anne Mione