Like What You Like or Like What Others Like? Conformity and Peer Effects on Facebook. Johan Egebark and Mathias Ekström - PDF

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IFN Working Paper No. 886, 2011 Like What You Like or Like What Others Like? Conformity and Peer Effects on Facebook Johan Egebark and Mathias Ekström Research Institute of Industrial Economics P.O. Box

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IFN Working Paper No. 886, 2011 Like What You Like or Like What Others Like? Conformity and Peer Effects on Facebook Johan Egebark and Mathias Ekström Research Institute of Industrial Economics P.O. Box SE Stockholm, Sweden Like What You Like or Like What Others Like? -Conformity and Peer Effects on Facebook Johan Egebark and Mathias Ekström October 14, 2011 Abstract Users of the social networking service Facebook have the possibility to post status updates for their friends to read. In turn, friends may react to these short messages by writing comments or by pressing a Like button to show their appreciation. Making use of five Swedish accounts, we set up a natural field experiment to study whether users are more prone to Like an update if someone else has done so before. We distinguish between three different treatment conditions: (i) one unknown user Likes the update, (ii) three unknown users Like the update and (iii) one peer Likes the update. Whereas the first condition had no effect, both the second and the third increased the probability to express a positive opinion by a factor of two or more, suggesting that both number of predecessors and social proximity matters. We identify three reasonable explanations for the observed herding behavior and isolate conformity as the primary mechanism in our experiment. Key words: Herding Behavior; Conformity; Peer Effects; Field Experiment JEL classification: A14; C93; D03; D83 1 Introduction Whenever a new trend arises be it within fashion, on product markets or even in politics it is relevant to ask if the popularity is explained by better quality or if it simply reflects a desire people have to do what everyone else does. The latter supposition, if true, has wide implications since it could explain, among other things, the formation of asset bubbles and dramatic shifts in voting Egebark: Department of Economics, Stockholm University and the Research Institute of Industrial Economics (IFN). Ekström: Department of Economics, Stockholm University. Acknowledgements: First we want to thank the Facebook users who made this experiment possible. We also want to express our gratitude to Pamela Campa, Stefano DellaVigna, Peter Fredriksson, Patricia Funk, Magnus Johannesson, Niklas Kaunitz, Erik Lindqvist, Martin Olsson and Robert Östling, as well as participants at the ESA Annual Meeting in Chicago 2011 and the National Conference of Swedish Economists in Uppsala 2011, for helpful discussions and valuable comments. Financial support from the Jan Wallander and Tom Hedelius Foundation is gratefully acknowledged. All remaining errors are our own. 1 behavior. Unfortunately, identifying herding behavior is by its nature difficult and hence we know little about the importance of this phenomenon. In this paper, we use the world s leading social networking service, Facebook, to study herding. Each Facebook user has a network of friends with whom he or she may easily interact through several different channels, e.g., by mailing, chatting or uploading photos or links. The most popular feature allows users to post status updates for friends to read; in turn, friends may react to these short messages by writing their own comments or by pressing a Like button to show they enjoyed reading it. We set up a natural field experiment to study whether users are more willing to Like an update if someone else has done so before. Making use of five Swedish users actual accounts, we create 44 updates in total during a seven month period. 1 For every new update, we randomly assign our user s friends into either a treatment or a control group; hence, while both groups are exposed to identical status updates, treated individuals see the update after someone (controlled by us) has Liked it whereas individuals in the control group see it without anyone doing so. We separate between three different treatment conditions: (i) one unknown user Likes the update, (ii) three unknown users Like the update and (iii) one peer Likes the update. Our motivation for altering treatments is that it enables us to study whether the number of previous opinions as well as social proximity matters. 2 The result from this exercise is striking: whereas the first treatment condition left subjects unaffected, both the second and the third more than doubled the probability of Liking an update, and these effects are statistically significant. We argue that conformity explains the behavior we observe in our experiment. Economists have defined conformity as an intrinsic taste to follow others (Goeree and Yariv, 2010), driven by factors such as popularity, esteem and respect (Bernheim, 1994). As Bernheim s model suggests, actions that are publicly observable signal predispositions and therefore affect status. Hence, if status concerns are sufficiently important to individuals, they will deviate from self-serving preferences and conform. 3 For many reasons, Facebook constitutes an environment where conformity potentially would occur. First it provides high visibility at any given time, a large number of users observe each other s actions allowing signaling to occur. Second, much of the activity on the website revolves around expressing attitudes and beliefs which are likely to be important for projecting status. Third, since there is no obvious way for users to disagree once a norm is established (i.e., no dislike option exist), conformity pressure is unlikely to weaken over time. Besides conformity, herding has been explained by: (i) correlated preferences, (ii) payoff externalities, (iii) limited attention and (iv) observational learning. We eliminate correlated effects due to the random assignment into treatment and control groups (see discussion in Cai et al., 2009). 1 The experiment took place between May and October The accounts we used were not created for the purpose of the experiment but rather borrowed from existing users. 2 Social impact theory developed in Latané (1981) lists three important factors determining the size of social influence: strength, immediacy and number. Moreover, previous findings from social psychology show that the more unanimous predecessors are, the more likely it is that subsequent decision-makers follow suit (Asch, 1955). Finally, there is convincing evidence that peers can play an important role in determining behavior (see e.g., Bandiera et al., 2005; Mas and Moretti, 2009; Sacerdote, 2001; Kremer and Levy, 2008). 3 Of course, people may also be inclined to express their independence by choosing a less popular option. Evidence of such behavior is found in Ariely and Levav (2000), Corazzini and Greiner (2007) and Weizsacker (2010). 2 Payoff externalities cause herding when each agent s actions affect other agents payoffs in such a way that an equilibrium arises. One typical example is right-hand (or left-hand) traffic. This explanation can also be ruled out as there is no reason for supposing such payoffs arise in our setting (the absence of any equilibrium in type or number of responses in our experiment as well as on Facebook in general speaks against this explanation). Arguably the other two mechanisms are more relevant in our experiment and we therefore address them in more detail. Limited attention is relevant in situations where agents make an optimal choice after delimiting the choice set (Huberman and Regev, 2001; Barber and Odean, 2008; Ariely and Simonsohn, 2008; DellaVigna and Pollet, 2009). We consider two cases saliency and searching. Status updates on Facebook that someone has responded to may be more salient because of their altered physical appearance (a blue rectangular area is added beneath the update). 4 Consequently, updates in treatment groups may have a higher probability of being read and this in turn could translate into more responses. However, the three treatment conditions we use affect the saliency of updates identically and since there is no treatment effect for one of the conditions, saliency cannot explain our results. Searching, on the other hand, would be an appropriate explanation if users want to save time or effort by looking for previous responses in order to find the best status updates quickly. Such screening will again increase the reading probability for updates in treatment groups, but since response behavior should be unaffected, we expect a treatment effect for both types of responses (Likes and comments). It turns out comments are unaffected in all of our treatment conditions and thus there is little support for the searching mechanism either. Observational learning models fit situations where successors follow those who are believed to be better informed because this constitutes a best response. 5 The key assumption is that agents obtain information by observing each other s actions and this in turn helps them maximize intrinsic utility (for theoretical studies, see Banerjee, 1992 and Bikhchandani et al., 1992; for empirical evidence, see e.g., Anderson and Holt, 1997 and Alevy et al., 2007). We argue that in our setting, where subjects choose whether to Like a status update or not, observational learning is unlikely to exist. The obvious reason being that choices in our experiment are made after subjects have experienced the product and have been able to evaluate it against comparable alternatives. In essence, people have all the tools required to make a private quality assessment instantly without the need for information signals. Although we are unable to directly address this channel, we present findings which support this argument. Our study is related to two different strands of literature within the economics discipline. On the one hand, we build on a growing body of experimental studies on herding behavior in different real-life settings, on the other, we tie in with the peer-effect literature. Cialdini et al. (1990), Goldstein et al. (2008), Ayres et al. (2009) and Chen et al. (2010) all study 4 The term saliency comes from neuroscience. The saliency of an item is the quality by which it stands out relative to its neighbors. 5 Note the difference between limited attention and observational learning. The former refers to a situation where subjects are certain of the quality of an update after reading it but use some (conscious or subconscious) rule to shrink the choice set. The latter, on the other hand, is relevant if subjects, after reading an update, are unsure of its quality. 3 decisions related to public goods (littering, resource usage and contributions to an online community). Hence, the effects found could be explained by either conformity or conditional cooperation, or both. Salganik et al. (2006) set up an artificial online music market and show that previous downloads positively affect an individual s tendency to download a specific song. The effect is mainly driven by the fact that frequently downloaded songs are listed higher up on the website, i.e., are more salient. When position is independent of number of downloads, the effect is weaker, suggesting that limited attention is the prime explanation for their results. Cai et al. (2009) vary information on menus in a Chinese restaurant chain to separate observational learning from saliency. Since there is an effect on demand when the five most popular dishes are displayed but not when five randomly dishes are highlighted, the authors interpret the results in favor of observational learning. Martin and Randal (2008) varies the amount of money (and mixture of coins and bills) in a transparent box, used to collect voluntary visiting fees in an art gallery, to analyze how visitors contributions are affected. However, no attempt is made to distinguish between different explanations. What separates our study is that we exploit a situation without payoff equilibria and where observational learning is negligible. Since status concerns are likely to be important, our focus is on conformity and how conformity pressures forms attitudes and beliefs. The research on peer effects has found that peers may play an important role in affecting for example productivity at work (Bandiera et al., 2005 and Mas and Moretti, 2009) and savings decisions (Duflo and Saez, 2002, 2003). We show that social proximity is also important when it comes to expressing conforming preferences. Moreover, contrary to many previous studies, we offer a precise definition of what we mean by a peer; rather than just saying he or she is a colleague or a roommate, we use the degree centrality condition. 6 This means we are able to explicitly study the role of what can be seen as a central person in a network of friends. In a broader sense, this study is motivated by a growing interest in social interactions within economics. Starting with Becker s (1974) critique of traditional economic theory for neglecting the importance of social interactions, researchers have long tried to gain further insights into this aspect of human life. According to Manski (2000), theorist s have succeeded quite well whereas empirical research is lagging behind, mostly because identification generally has been too challenging. Manski s main conclusion is that more knowledge would be gained with well-designed experiments in controlled environments (Soetevent, 2006, concludes with a similar argument). Our hope is that this study will increase our understanding of herding behavior in general and conformity in particular, and encourage further research on these and other related topics. The paper evolves as follows. Section 2 describes Facebook while section 3 presents the experimental design. Section 4 and 5 summarizes the data and presents the main findings. In Section 6 we discuss our results in a broader context and section 7 concludes. 6 AseminalpaperondifferentcentralityconditionsisFreeman(1979). 4 2 About Facebook Facebook is the leading social networking site. 7 The largest countries according to number of total users are the US (150 million), Indonesia (34 million) and the UK (28 million). Sweden has around 4 million users meaning penetration is about the same as in North America (somewhere between percent). 8 Currently, the website is the second most visited of all and it has attracted increasing attention as a marketing channel both within politics and the corporate environment. The company s own statistics report that the average user has 130 friends, spends over one day per month on Facebook and creates 90 pieces of content each month (e.g., links, blog posts, notes and photo albums). Moreover, 50 percent of what Facebook defines as active users log on to the website in any given day. Ultimately, Facebook is an arena for people who seek to interact with their network of friends. Other users are added to your network when they accept your Friend Request. Once you have become friends you may visit each other s profiles and can easily interact through different channels, e.g. by mailing, chatting or uploading photos or links. The most popular feature, Status, allows users to inform their friends of their whereabouts and actions in status updates. These short messages are made visible to friends on the News Feed which displays updates as Most Recent or as Top News. 9 Immediately after it has been posted, friends may react to a status update either by writing their own comments or by pressing a Like button to show their appreciation. Both types of responses show up together with the update and are thus clearly visible to the user who wrote the update and his or her network of friends. A status update is limited to 420 characters (including spaces), which means they are typically short, in most cases one sentence. Moreover, a majority of updates are current they reveal for example what the user is doing right now or where he or she is which means any reactions from friends are unlikely to show up after more than one or two days (the fact that none of the updates we used in the experiment generated any response after 20 hours confirms this). The Like button was introduced in February 2009 and quickly became a widely popular way for users to express positive opinions about shared content. Facebook s own description of how this feature works is as follows: We ve just introduced an easy way to tell friends that you like what they re sharing 7 comscore reports the website attracted 130 million unique visitors in May 2010 and Goldman Sachs estimates the website has more than 600 million users in total as of January The exact numbers varies depending on source. Figures reported here are from CheckFacebook.com which, although not affiliated with Facebook, claims to use data from its advertising tool. 9 When we ran the experiment in 2010, users could choose between the alternative views themselves and easily change between them (see Figure 5 in the Appendix). Moreover, according to Facebook, the Top News algorithm was based on how many friends are commenting on a certain piece of content, who posted the content, and what type of content it is (e.g. photo, video, or status update) (see This means the number of Likes did not determine if a status update appeared on Top News or not. During the experiment, we confirmed that this was the case. In light of this, it should be mentioned that Facebook continuously changes the interface of the website (arguably in order to develop and improve its functionality). A major change occurred in September 2011 which altered the way the News Feed presents information. This means there are some discrepancies between the presentation of Facebook in this paper (which is based on how things were in 2010) and its current format. Importantly, no major changes were made during the experimental period in on Facebook with one easy click. Wherever you can add a comment on your friends content, you ll also have the option to click Like to tell your friends exactly that: I like this. Leah Pearlman, The Facebook Blog We include a print screen in the Appendix to show the way the News Feed presents recent status updates in reverse chronological order (Figure 5). As seen in this figure, the first update has no responses while the second has received a comment and the third a Like. 3 The Experiment Posting a status update on Facebook usually means all of your friends can see it. However, each user has the possibility to control who sees a specific update through privacy settings. Thus, if users wish, they can create a subset of friends, e.g., family members or close friends, and write the message to this group only. We use this feature in our experiment since it allows us to post identical status updates, simultaneously, to different groups in our case treatment and control groups. Importantly, members of a group can only follow the communication within the specific group and this communication is displayed as normal to the selected members. Hence, we do not worry that subjects perceive the status updates we post within the experiment differently from the ordinary stream of information on the News Feed. We post 44 status updates in total during a seven month period using five Swedish Facebook accounts. 10 Table 1 briefly describes the six steps we go through each time we post an update (every time we execute the process, we use one account which means the 44 updates are distributed over the five accounts). We do not want updates to stand out but rather be a natural part of the ongoing communication on the website. Therefore, in the first step, we ask one of our five users to text his or her status update to us whenever we decide it is time. 11 From the list of examples given in Table 7 in the Appendix, we see that updates are trivial in the sense that they are short, easy to interpret and do not say anything which could be perceived as sensitive, such as political opinions or religious views. 12 In the second step, after receiving the content for a status update, we randomly draw one of three types of treatment conditions: (i) on
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