Intrinsic versus Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?

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1 1 Intrnsc versus Image-Related Utlty n Socal Meda: Why Do People Contrbute Content to Twtter? Olver Touba Glaubnger Professor of Busness Columba Busness School 522 Urs Hall, 3022 Broadway, New York, NY ot2107@columba.edu Andrew T. Stephen Assstant Professor of Busness Admnstraton & Katz Fellow n Marketng Unversty of Pttsburgh, Joseph M. Katz Graduate School of Busness 318 Mervs Hall, Pttsburgh, PA astephen@katz.ptt.edu forthcomng, Marketng Scence

2 2 Intrnsc versus Image-Related Utlty n Socal Meda: Why Do People Contrbute Content to Twtter? We emprcally study the motvatons of users to contrbute content to socal meda n the context of the popular mcrobloggng ste Twtter. We focus on non-commercal users who do not beneft fnancally from ther contrbutons. Prevous lterature suggests two man sources of utlty that may motvate these users to post content: ntrnsc utlty and mage-related utlty. We leverage the fact that these two types of utlty gve rse to dfferent predctons as to whether users should ncrease ther contrbutons when ther number of followers ncreases. To address the ssue that the number of followers s endogenous, we conducted a feld experment n whch we exogenously added followers (or follow requests n the case of protected accounts) to a set of users over a perod of tme, and compared ther postng actvtes to those of a control group. We estmated each treated user s utlty functon usng a dynamc dscrete choce model. Whle our results are consstent wth both types of utlty beng at play, our model suggests that magerelated utlty s larger for most users. We dscuss the mplcatons of our fndngs for the evoluton of Twtter and the type of value frms may derve from such platforms n the future. Keywords: Socal Meda, Feld Experments, Dynamc Dscrete Choce Models.

3 3 1. Introducton In recent years, socal meda has emerged as a major channel for broadcastng nformaton. For nstance, by late 2011 there were over 173 mllon publc blogs, 1 and 250 mllon messages ( tweets ) were sent each day through the popular mcrobloggng platform Twtter. 2 Although some contrbutors to socal meda are able to derve advertsng revenue from ther content (usng for example platforms such as Google s AdSense; cf. Sun and Zhu 2011), socal meda platforms rely predomnantly on the benevolent contrbutons of mllons of ndvduals as content provders. Whle publshers ncentves n tradtonal meda are well understood and are typcally a functon of the number of eyeballs reached by ther content, motvatons to benevolently contrbute content n socal meda are not well understood. A socal meda platform may be utlzed by a frm for dfferent (non-exclusve) purposes. For example, t may be used as a meda outlet (.e., the frm broadcasts content to consumers), a vral marketng platform (.e., the frm nduces consumers to share nformaton about ts brands wth other consumers and/or tracks naturally occurrng word of mouth), or a customer nsghts platform (.e., the frm montors consumers conversatons). We argue that a frm cannot decde how to leverage socal meda and devse a fully effcent socal meda strategy unless t understands what motvates consumers to be actve on such platforms n the frst place. Moreover, for the platforms themselves, understandng what motvates ther users to contrbute s mportant snce the vablty of these platforms as busnesses depends not only on how many users they have but also on how actve ther users are as content contrbutors. However, extant marketng research on socal meda and related phenomena such as onlne word of mouth has 1 Nelsen Blogpulse, October 7, Medabstro, October 18,

4 4 focused prmarly on the outcomes of user actvty, and less on the motvatons underlyng user actvty (e.g., Godes and Mayzln 2004; Trusov, Buckln, and Pauwels 2009; Katona, Zubcsek, and Sarvary 2011; Stephen and Galak 2012). In the absence of explct economc ncentves, the lterature suggests two relevant types of utlty that may motvate non-commercal socal meda users to contrbute content: ntrnsc utlty and mage-related utlty. Intrnsc utlty assumes that users receve drect utlty from postng content, and leads to the dong of an actvty for ts nherent satsfactons rather than for some separable consequence (Ryan and Dec 2000). Image-related utlty, on the other hand, assumes users are motvated by the perceptons of others (see Fehr and Falk 2002 for a revew of the psychologcal foundatons of ncentves). 3 Image-related utlty s also related to status seekng or prestge motvaton (e.g., Glazer and Konrad 1996; Harbraugh 1998a, 1998b; Fershtman and Gandal 2007; Lampel and Bhalla 2007). Intrnsc and mage-related utlty have been studed qute extensvely n the doman of prosocal behavor (see for example Glazer and Konrad 1996; Harbaugh 1998a, 1998b; Bénabou and Trole 2006; Arely, Bracha and Meer 2009). In a doman closer to socal meda, Lerner and Trole (2002, 2005) contrast the ntrnsc pleasure open-source developers derve from workng on cool projects wth the (mage-related) desre for peer recognton. See also Btzer, Schrettl and Schröder (2007) or von Hppel and von Krogh (2003) for a theoretcal dscusson of the motvatons to contrbute to open-source projects, and von Krogh and von Hppel (2006) for a revew. Several papers have provded addtonal survey-based emprcal evdence that ntrnsc and mage-related utlty are ndeed relevant n open-source development (e.g., Ghosh et al. 2002; Hars and Ou 2002; Lakhan and Wolf 2005; Roberts, Il-Horn Hann and Slaughter 2006). 3 Fehr and Falk also dscuss recprocty as a psychologcal source of motvaton, whch depends on whether an agent perceves the acton of another agent as hostle versus knd. Ths s less relevant n our context.

5 5 Survey-based evdence for ntrnsc and mage-related utlty has also been found n the context of electronc knowledge repostores (see for example Kankanhall, Tan and We 2005; Wasko and Faraj 2005; Lampel and Bhalla 2007; Nov 2007). In the doman of socal meda specfcally, Bughn (2007) surveys users of an onlne vdeo-sharng ste and fnds that ther prmary motvatons to upload vdeos are mage-related ( I seek fame ) and ntrnsc ( It s fun ). Henng-Thurau et al. (2004) survey the motvatons of contrbutors to web-based opnonplatforms. Besdes some motvatons specfc to ther partcular context, the motvatons found by these authors tend to be ether ntrnsc (e.g., It s fun to communcate ths way wth other people n the communty ) or mage-related (e.g., My contrbutons show others that I am a clever consumer ). Therefore, based on the extant lterature s appears that ntrnsc and mage-related utlty are both plausble and realstc motvatons for people to contrbute content n socal meda. However, to the best of our knowledge the emprcal evdence to date s only survey-based. In ths paper we compare these two types of utlty usng a dfferent emprcal approach, focusng specfcally on the context of the popular mcrobloggng platform Twtter. 4 Twtter s an deal socal meda context n whch to emprcally study ntrnsc and mage-relate utlty, because these two types of utlty gve rse to opposte predctons as to whether users should ncrease or decrease ther postng actvtes when ther number of followers ncreases. In order to address the ssue that the number of followers s endogenous, we conducted a feld experment n whch we exogenously added followers (or follow requests n the case of protected accounts) to a set of users (treatment group), and compared ther postng actvtes to those of a control group. 4 Whereas a blog s a webste or part of a webste that dsplays entres or elements of content (text, graphcs, vdeo, etc.) usually posted by an ndvdual, a mcroblog s a type of blog that allows users to exchange smaller elements of content (e.g., short sentences, ndvdual mages, lnks).

6 6 We report two sets of analyses n ths paper. An ntal model-free analyss of our data shows that whle our nterventon dd not have a statstcally sgnfcant man effect, t had a sgnfcant postve effect on postng actvtes for treated users wth a moderately low ntal number of followers and a sgnfcant negatve effect for treated users wth a moderately hgh ntal number of followers. These fndngs are consstent wth both ntrnsc and mage-related utlty beng relevant, wth the domnant motvaton beng dfferent for users wth dfferent numbers of followers. Whle havng the beneft of beng free of any functonal form assumpton, ths modelfree analyss does not allow us to quantfy the relatve magntudes of these two types of utlty and how they vary across users based on observed and unobserved factors. Accordngly, we then analyze our data usng a dynamc dscrete choce model. Ths allows us also to make counterfactual predctons on the evoluton of the Twtter platform as the network becomes stable. Two recent papers related to our research are Kumar (2009) and Shrver, Nar, and Hofstetter (2012). Kumar (2009) studes consumers purchase of rng-back tones for ther moble phones (a rng-back tone s not consumed by the user purchasng t but rather by those who call that user), and estmates the utlty consumers derve from havng a hgh status (.e., more recently updated tones), from consumng the tones purchased by ther peers, and from expressng themselves through the tones they purchase. Shrver et al. (2012) study the causal relatons between content generaton and the number of socal tes n an onlne wndsurfng communty, usng a set of nstrumental varables. However, nether paper studes specfcally the utlty derved from postng content n socal meda. Kumar (2009) uses a context slghtly dfferent from socal meda, and Shrver et al. (2012) do not study utlty or motvaton drectly.

7 7 The remander of the paper s organzed as follows. In Secton 2, we provde an overvew of the Twtter platform, dscuss how the concepts or ntrnsc and mage-related utlty are operatonalzed n ths context, and descrbe our emprcal strategy. We descrbe our data n Secton 3, provde some model-free analyss n Secton 4, and analyze the data usng a dynamc dscrete choce model n Secton 5. We conclude n Secton Background 2.1. Twtter Twtter s a very popular socal meda platform that allows users to share tweets (text messages up to 140 characters long) wth ther followers (other users who choose to subscrbe to a user s feed of tweets). The ablty to follow other users creates a drected socal network (unlke other socal networks such as Facebook or Lnkedn whch are undrected networks, user A followng user B on Twtter does not automatcally mply that B follows A). A user s home page (as seen by that user) contans a tmelne that captures all the tweets posted by the users ths user follows (n reverse chronologcal order), a text box labeled what s happenng that allows the user to post a tweet, and a remnder of the number of users followng the user and the number of users followed by the user. 5 Twtter users may be splt nto non-commercal and commercal users. Commercal users may be classfed nto celebrtes, meda organzatons, non-meda organzatons, and brands (Wu et al. 2011). In ths paper we focus on non-commercal users for whom there exsts no apparent fnancal ncentve to contrbute content. 5 Besdes typng a message n the What s happenng wndow of ther home page, users may also post tweets as reples or retweets. A reply to a prevous tweet s a text message of up 140 characters that wll be seen by users who follow both the user who posted the ntal tweet and the user replyng to that tweet. A retweet forwards a prevous tweet to a user s followers. In our data tweets ncludes tweets, retweets and reples.

8 8 Other features of Twtter nclude the ablty for a user to unfollow another user (.e., stop followng a user that he or she had been followng), to block another user (.e., prevent that other user from followng hm or her), and to make hs or her own account protected. Accounts that are not protected are called publc (ths s the default settng) and may be followed and accessed by any user. If a user elects to protect hs or her account, then requests by other users to follow that account need to be approved by hm or her, and the text of hs or her tweets may only be accessed by hs or her followers. However, the number of users followed, number of followers, and cumulatve number of tweets are publc nformaton both for publc and protected accounts. Accordng to Cha et al. (2010), approxmately 8% of Twtter accounts are protected. One fnal characterstc of Twtter that s crtcal to our analyss s that postng content s a way for users to attract new followers. Ths clam s supported by our data (.e., the state transton probabltes reported n Secton 5.1.4), and s consstent wth Shrver et al. (2012) who fnd a postve causal effect of content generaton on the number of socal tes n an onlne wndsurfng communty. Note that unlke other drected socal networks, recprocty (.e., A follows B and B follows A) on Twtter s only moderate. Kwak et al. (2010) report that of all user pars on Twtter wth at least one lnk between them, only 22.1% have a recprocal relatonshp (.e., each user n the par follows the other user). In other words, a user s number of followers s not smply a by-product of hs or her followng actvtes, and postng content n the form of tweets s one way for users to attract new followers. Twtter usage has been steadly growng. The number of unque US vstors to twtter.com n September 2011 was estmated at 35 mllon, up from 28 mllon n September In March 6 Source: accessed November 15, 2011.

9 the number of actve users throughout the world was reported to be 140 mllon. 7 The average number of tweets per day grew from an average of 5,000 n 2007 to 300,000 n 2008 to 2.5 mllon n In October 2011 ths number reached 250 mllon tweets per day 8 and n March 2012 t reached 340 mllon tweets per day. 9 Even f each tweet takes only a few seconds to wrte, wth 340 mllon tweets wrtten per day, the equvalent of multple decades of one person s lfe are spent each day postng content on Twtter (340 mllon tweets tmes 5 seconds per tweet = 53.9 years). Gven the scale and relevance of Twtter n socety, t s not surprsng that academc research on Twtter has started to emerge, mostly from Computer Scence and Informaton Systems. Extant research has focused prmarly on studyng the structure and the nature of the Twtter socal network, and on ssues related to nfluence and nformaton dffuson on ths network (see for example Cha et al. 2010; Kwak et al. 2010; Weng, Lm and Jang 2010; Bakshy et al. 2011; Romero et al. 2011; Wu et al. 2011; Goel, Watts, and Goldsten 2012). However, to the best of our knowledge academc research on Twtter n marketng and other socal scences to date has been lmted. Exceptons nclude Ghose, Goldfarb, and Han (2011) who compare user search costs n onlne versus moble platforms usng data from a mcrobloggng ste comparable to Twtter; and Stephen, Dover, Muchnk, and Goldenberg (2012) who study how user actvty on Twtter affects the extent to whch URLs posted by users n tweets spread through the Twtter network. 7 Source: accessed October 9, Medabstro, October 18, Source: accessed October 9, 2012.

10 Intrnsc versus mage-related utlty on Twtter Intrnsc utlty Twtter s ntal postonng was as a real-tme nformaton network powered by people all around the world that lets you share and dscover what s happenng now (twtter.com/about, accessed 02/2010). Twtter further states that Twtter asks what s happenng and makes the answer spread across the globe to mllons. The publc nature of Twtter and the clams that the nformaton spreads to mllons across the globe suggest that the ntrnsc utlty derved by a non-commercal user from postng content on Twtter should be monotoncally non-decreasng n that user s number of followers. Put smply, a user should derve more ntrnsc utlty from broadcastng content as the sze of hs or her audence ncreases. Ths s smlar to the case where content publshers receve explct fnancal ncentves, whch are typcally monotoncally nondecreasng n the sze of the publsher s audence. Whle not crtcal to our argument, we also assume that ntrnsc utlty from postng content s concave n the number of followers Image-related utlty The defnton of mage-related utlty on Twtter should not be lmted to the management of the user s mage (.e., how the user s portrayed on the platform). Instead, mage-related utlty should be defned more broadly, to encompass the sense of self-worth and socal acceptance provded by a user s actvtes on the platform. In partcular, there s some evdence suggestng that mage-related utlty on Twtter s related to a user s number of followers. Whle any user s able to contrbute as much content and follow as many users as he or she wants, followers need to be earned and a user s number of followers s an nformatve socal sgnal. The number of followers has been used as a measure of nfluence by academcs (Cha et al. 2010; Kwak et al. 2010) and s often assocated wth

11 11 popularty by the general publc (e.g., wefollow.com). 10 There have been several reports n the press of Twtter users attachng a lot of mportance to ther number of followers. Accordng to Polett (2009), Twtter has become an avenue for self-promoton, and one s number of followers s becomng the new barometer of how we gauge our self worth. 11 Leonhardt (2011) clams that the number of followers on Twtter s just how people keep score on the ste and compare themselves to frends and colleagues. 12 Tetell (2011) reports on the socal pressures to acheve hgh numbers of followers on Twtter and hgh scores on stes such as and that rate all Twtter users based on ther number of followers (as well as other metrcs, usng propretary scorng rules). 13 The mportance for many Twtter users of havng a large number of followers s further revealed by the plethora of webstes that offer advce on how to ncrease that number (a partal lst may be obtaned by typng ncrease Twtter followers n any search engne). Therefore t seems approprate to measure the stature or prestge of a Twtter user by hs or her number of followers. It s reasonable to model utlty from stature as a non-decreasng concave functon of the number of followers. For example, Baumester and Leary (1995) argue that humans have a fundamental need for a certan mnmum number of socal bonds, but that the formaton of further socal attachments beyond that mnmal level should be subject to dmnshng returns; that s, people should experence less satsfacton on formaton of such extra relatonshps, (Baumester and Leary 1995, p. 500) and they revew emprcal evdence supportng ths clam. Also consstent wth mage-related utlty beng concave, DeWall, Baumester and Vohs (2008) 10 See also Beck, Howard New Way to Gauge Popularty. The New York Tmes, October Polett, Therese What f your frends won t follow you on Twtter? MarketWatch, November Leonhardt, Davd A Better Way to Measure Twtter Influence. The New York Tmes, March Tetell, Beth Ascent of the Socal-Meda Clmbers. The Boston Globe, February 18.

12 12 provde expermental evdence that satatng the need for socal acceptance leads to a reducton n the drve to satsfy that need. In summary, both ntrnsc utlty from postng content and mage-related utlty from havng many followers may be assumed to be monotoncally non-decreasng and concave n the user s number of followers. However, one key dfference s that whle ntrnsc utlty s derved from postng content vewed by many followers, mage-related utlty s derved from havng many followers. If a user does not post content on a gven day, he or she wll obvously not derve any ntrnsc utlty from postng content on that day. On the other hand, mage-related utlty from havng many followers s a measure of stature whch s ndependent of contemporaneous postng actvtes. We wll see next how, as a result of ths dfference, the motvaton to post content (.e., the total expected ncremental utlty derved from postng content on a gven day) takes a dfferent form under ntrnsc versus mage-related utlty Emprcal strategy Twtter offers a unque opportunty to study and contrast ntrnsc and mage-related utlty n socal meda for at least two reasons. Frst, by focusng on non-commercal users we are able to study contrbutons to socal meda n a context n whch fnancal or other extrnsc ncentves are mnmal, f present at all. Second and most mportantly, Twtter provdes a context for emprcally comparng ntrnsc versus mage-related utlty, because they gve rse to dfferent predctons as to how users should react to an ncrease n ther number of followers. Throughout ths secton we llustrate the opposte predctons made by ntrnsc vs. magerelated utlty wth a hghly stylzed and smplfed model. Ths two-perod model s presented only for llustraton purposes and s not used anywhere else n the paper. We show n Appendx 1

13 13 that smlar results are obtaned wth an nfnte-horzon verson of ths model and llustrate the results graphcally. Let n denote a user s number of followers. Let U(n) be the per-perod utlty derved from postng content to n followers n a gven perod (e.g., day). In the case of ntrnsc utlty, ths utlty s derved n a gven perod only f content s posted n that perod. On the other hand, n the case of mage-related utlty, ths utlty s derved n a gven perod rrespectve of whether content s posted n that perod. We assume that U(n) s monotoncally ncreasng and concave n n. We further assume (for the purposes of ths llustratve model only) that f a user posts content n Perod 1, hs or her number of followers wll ncrease to n+1 n the next perod wth probablty δ, and stay the same wth probablty (1- δ). If content s not posted n Perod 1, we assume that the number of followers wll reman the same n the next perod. (Note that n Secton 5 we use emprcal state transton probabltes nstead of makng these smplfyng assumptons.) Table 1 lsts the utlty derved by the user n each perod as a functon of hs or her acton n each perod, n the case of ntrnsc utlty. Table 2 does the same, n the case of mage-related utlty. [ Insert Tables 1 and 2 Here ] Intrnsc utlty: mplcaton when number of followers ncreases If users contrbute content to Twtter because of the ntrnsc value they derve from broadcastng nformaton to ther followers, and f the utlty derved from postng s monotoncally nondecreasng and concave n a user s number of followers, then we should expect users to ncrease ther postng actvtes as they receve addtonal followers. Qute smply, f utlty from postng content s ncreasng n the number of followers, havng more followers should lead to more postng.

14 14 In terms of our llustratve model, we see n Table 1 that the user derves an addtonal utlty U(n) by postng n perod 1, whch s monotoncally ncreasng n n by assumpton. In case the user also posts n Perod 2, then postng n Perod 1 ncreases the expected utlty derved n Perod 2 from U(n) to δu(n+1)+ (1-δ)U(n) (because of the potental ncrease n the number of followers due to postng n Perod 1). In that case postng content n Perod 1 provdes a total (over both perods) expected addtonal ntrnsc utlty of U(n)+[δU(n+1)+(1-δ)U(n)- U(n)]=δU(n+1)+(1-δ)U(n). Ths quantty s also monotoncally ncreasng n n, because δ 0, 1- δ 0 and U(n) s monotoncally ncreasng n n. In other words, the total expected ncremental utlty from postng content n Perod 1 s ncreasng n n, rrespectve of whether the users posts content n Perod Image-related utlty: mplcaton when number of followers ncreases. Wth mage-related utlty, postng content s not the drect source of utlty, but rather a means towards an end,.e., a way to attract new followers. The utlty comes from havng many followers, not from broadcastng content to them. Postng content on a gven day nfluences future expected mage-related utlty, by ncreasng the expected number of followers the user wll have n the future. Therefore, n contrast to ntrnsc utlty, the ncremental mage-related utlty acheved by postng content on a gven day wll be derved n the future, and s based on the addtonal followers the user wll gan by postng that day. If there are dmnshng returns to addtonal followers, ths ncremental future expected utlty s decreasng n the current number of followers. Therefore the motvaton to post content n order to attract new followers should be decreased as the current number of followers s ncreased One may thnk that users who are motvated by mage would feel compelled to actually post more as they amass more followers, n order to mantan ther number of followers. However, our data suggest that the expected change n the number of followers when no postng occurs s not negatve (see state transton probabltes reported n Sec-

15 15 In terms of our llustratve model, f the user posts content n Perod 1, there s a probablty δ that mage-related utlty n Perod 2 wll be ncreased from U(n) to U(n+1). As shown n Table 2, postng content n Perod 1 provdes an addtonal total expected mage-related utlty of δ(u(n+1)-u(n)), whch s realzed n Perod 2 rrespectve of whether the user posts content n Perod 2. Ths llustrates that under mage-related utlty, the ncremental beneft from postng content on a gven day s realzed n the future, and s a result of attractng new followers. Because U(n) s concave n n, U(n+1)-U(n) s decreasng n n,.e., the ncremental total expected mage-related utlty derved from postng content n Perod 1 s decreasng n the number of followers at the begnnng of Perod 1. Interestngly, under mage-related utlty, the ncremental beneft from postng content n a gven day s ncreasng n the lkelhood that postng content wll ncrease the number of followers (parameter δ n our llustratve model). Therefore, users motvated by mage-related utlty should also post less content as the structure of the network becomes stable (.e., a non-evolvng statc structure of connectons s acheved) and as postng actvtes become less lkely to lead to addtonal followers. Ths rases questons on the longer-term sustanablty of the Twtter platform, and has mplcatons for the type of value frms may be able to derve from socal meda n the future. Ths ssue wll be addressed usng counterfactual analyses n Secton In sum, ntrnsc utlty from postng content and mage-related utlty from havng many followers gve rse to opposte predctons as to how users should react to an ncrease n ther number of followers. If users are motvated by the ntrnsc utlty from broadcastng content to many followers, then havng more followers should lead to an ncrease n postng actvtes. On the other hand, f users derve ther utlty from havng many followers and post content n order ton 5.1.4), so ths scenaro seems unlkely. More generally, our model n Secton 5 wll enable us to take such scenaros nto account, by explctly capturng and quantfyng the mpact of postng on a user s future number of followers.

16 16 to gan addtonal followers, then the motvaton to post content should be dmnshed as the current number of followers s ncreased (due to addtonal followers havng dmnshng returns). In Appendx 1, we show how the results of the smple two-perod llustratve model used here generalze to an nfnte horzon, and we llustrate graphcally how the ncremental value from postng content n a gven perod vares wth the number of followers, under ntrnsc and mage-related utlty Data Our data were collected drectly from Twtter usng Twtter s applcaton programmng nterface (API; see dev.twtter.com). We selected a random set of 2,493 non-commercal Twtter users from an ntal database of approxmately 3 mllon user accounts. We ensured that our users were non-commercal by checkng account names, and checkng aganst lsts and classfcatons on stes such as wefollow.com and twtterholc.com. The users n our dataset are a mx of publc and protected accounts. We collected data daly on the followng varables for each user n our sample: () the number of followers, () the number of users followed, and () the cumulatve number of tweets posted by that user snce the account was created. Unfortunately, the structure of the socal network to whch these users belong was not avalable to us Intal calbraton dataset We frst collected data daly for these 2,493 users for 52 days, between May 8, 2009 and June 28, Ths ntal dataset allowed us to dentfy actve users among the set. We classfed a user 15 We note that there are condtons, related to the way postng affects one s future number of followers, under whch ntrnsc and mage-related utlty could n fact have the opposte effects to those just descrbed. These condtons have low face valdty and are dscussed at the begnnng of Secton 5. Notwthstandng, our model n Secton 5 enables us to quantfy ntrnsc versus mage-related utlty even under such condtons. In partcular, the dentfcaton of our model does not rely on the assumpton that ntrnsc (mage-related) utlty always gves rse to an ncrease (decrease) n postng actvty followng an ncrease n the number of followers.

17 17 as actve f he or she ncreased hs or her cumulatve number of tweets or number of users followed at least once durng ths screenng observaton wndow. Out of all users, 1,355 were classfed as actve Feld experment We collected daly data agan from the same set of 2,493 users for 160 days, between September 14, 2009 and February 20, 2010 (our man observaton wndow). We selected 100 users randomly from the set of 1,355 actve users as our treatment group. In order to ntroduce exogenous varatons n the number of followers, we gradually added 100 followers to publc accounts n the treatment group over a 50-day perod (days 57 to 106). For protected accounts n the treatment group, we sent 100 follow requests over the same 50-day perod. In order to execute our treatment, we created and managed 100 synthetc Twtter users (50 males, 50 females) and created one lnk from each synthetc user to each treated user (.e., followed or sent a follow request) between days 57 and 106. Wth the help of two undergraduate research assstants who were avd Twtter users, we attempted to make our synthetc users as realstc as possble (we wll test the realsm of these users expermentally n Secton 4.3). The names of the synthetc users were generated usng the name generator avalable at Before lnkng to the treated users, profle pctures were uploaded to the synthetc users profles and each synthetc user followed an average of fve other synthetc users as well as some celebrtes and meda organzatons (as s typcal for many Twtter users). The synthetc users also posted tweets on a regular bass. In order to ncrease the credblty of the exogenous lnks to the treated users from the synthetc users, we started by creatng one lnk (.e., addng one synthetc follower or sendng one follow request n the case of protected accounts) per day to each treated user. After dong so each day for four days, we

18 18 ncreased the daly number of exogenous lnks per treated user to two per day, and so on untl the rate ncreased to fve per day for four days, after whch t was decreased to four per day for four days, and so on. By day 106 each synthetc user had created one lnk to each treated user. Fgure 1 shows the number of exogenous lnks created to each treated user on each of the 160 days n our man observaton wndow. 16 Note that our expermental procedure respects Twtter s Terms of Servce (avalable at twtter.com/tos). [ Insert Fgure 1 Here ] 4. Model-free analyss 4.1. Descrptve statstcs We frst report some key descrptve statstcs. Fgure 2 shows hstograms and log-log plots of the dstrbuton of the number followers on the frst day of the man observaton wndow, for all 2,493 users and for all 1,355 users who were actve durng the screenng perod (.e., the set of users from whch our treated users were drawn). The dstrbuton of the number of followers s close to a truncated power-law (the log-log plots are close to lnear), whch s typcal of socal networks (e.g., de Solla Prce 1965; Barabás and Albert 1999; Stephen and Touba 2009). Fgure 3 shows the dstrbuton across all 1,355 actve users of the average daly postng rate durng the man observaton wndow. The average daly postng rate s measured as the total number of posts durng the wndow dvded by the number of days. We see that the dstrbuton s heavly skewed, wth many users postng very lttle and few users postng heavly. Fgure 4 shows the evoluton of the medan number of followers over tme for treated versus control users. Fgure 5 shows the dstrbuton, among treated users only, of the dfference between the numbers of 16 The gaps n Fgure 1 are due to our RAs needng to take breaks from ths labor-ntensve actvty.

19 19 followers at the end versus the start of the nterventon (day 107 mnus day 57). We see that the control and treatment groups had very comparable medan numbers of followers before the start of the nterventon (days 1-57). We also see that the actual ncrease n number of followers for treated users may be larger than 100 (due to the addton of organc new followers) or smaller than 100, because some treated users had protected accounts and dd not accept all synthetc users follow requests, and because all users have the ablty to block any of ther followers. 17 Nevertheless, by the end of the man observaton wndow, the medan number of followers for treated users was greater than the medan number for non-treated users by a margn of In order to verfy that the randomzaton between treatment and control groups was done approprately, we conducted non-parametrc rank sum tests comparng the number of followers on day 1, the number of users followed on day 1, and the average daly postng rate before treatment (days 1 to 56) for treated versus non-treated users. None of these tests were sgnfcant (all p > 0.16). Smlar results were obtaned wth two-sample t-tests (all p > 0.20). We also compared the dstrbutons of the number of followers on day 1 usng the Kolmogorov-Smrnov (KS) statstc. The two dstrbutons are not statstcally sgnfcantly dfferent (p > 0.34). 18 [ Insert Fgures 2-5 Here ] 4.2. Impact of nterventon on postng actvty We now consder the postng behavor of treated versus control users. Studyng how treated users reacted to our nterventon s nterestng and relevant n and of tself for Twtter and other socal meda platforms. Moreover, as argued earler, ncreasng (decreasng) postng actvty 17 The correlaton coeffcent between the number of followers on day 1 and the ncrease n number of followers s not sgnfcant (ρ=0.126, p-value>0.21). 18 Because the KS test tself only apples to contnuous dstrbutons, we use bootstrappng to determne the correct p-value. A smlar p-value s obtaned usng a standard KS test.

20 20 followng the addton of new followers s consstent wth ntrnsc utlty (mage-related utlty). We note that we can only argue that each reacton s consstent wth a dfferent type of utlty, not equvalent to t. As s often the case n socal scences, we do not observe or measure users motvatons drectly, and nstead we dsentangle dfferent sources of motvaton by dentfyng a settng n whch they make dvergent predctons. However we acknowledge that we cannot rule out all alternatve explanatons for the behavor of our treated users. We compare each user s average daly postng rate after the nterventon (days 107 to 160) to before the nterventon (days 1 to 56). We fnd that the proporton of users for whom the average daly postng rate ncreased after the nterventon s somewhat greater among treated users than t s among the control users. Specfcally, 40.82% of treated users had a greater postng rate after the nterventon than before, compared to 34.19% of control users. However, the dfference between these two proportons s not statstcally sgnfcant (Z = 1.32, p = 0.19). 19 Therefore, our nterventon dd not have a sgnfcant man effect on postng actvty. We now explore whether our nterventon had dfferent effects based a user s ntal number of followers. Ths s plausble for at least two reasons. Frst, we should expect ntrnsc and mage-related utlty to vary dfferently as a functon of a user s number of followers. Therefore, whle the behavor of a user may be more consstent wth one source of utlty when that user has few followers, t may be more consstent wth the other source as the number of followers ncreases. Second, there s lkely heterogenety across users n the relatve mportance of magerelated versus ntrnsc utlty, and ths heterogenety may be reflected n the number of followers. For example, users for whom mage-related utlty s prevalent may be more lkely to 19 Consstent wth ths result, the average daly postng rate after the treatment (days 107 to 160) s not statstcally sgnfcantly dfferent for treated vs. non-treated users (Wlcoxon rank sum test, z=0.935, p >0.35; two-sample t-test, t=1.32, p > 0.18).

21 21 have made an effort to amass larger numbers of followers. Both of these factors would lead to users wth dfferent numbers of followers reactng dfferently to the treatment. Fgures 6 and 7 respectvely plot the probabltes that a user ncreased and decreased hs or her postng rate after vs. before the nterventon, as a functon of the log of that user s ntal number of followers (on day 1 of the man observaton wndow), for treated and non-treated users. These fgures were obtaned by smoothng the raw data usng a Gaussan kernel functon (bandwdth = 1). 20 We see that treated users wth lower ntal numbers of followers tended to ncrease ther postng rates relatve to control users. However, treated users wth hgher ntal numbers of followers tended to decrease ther postng rates relatve to control users. [ Insert Fgures 6-7 Here ] In order to statstcally compare the mpact of the treatment on postng behavor as a functon of the ntal number of followers, we splt our treated users nto quntles based on ther ntal numbers of followers on day 1 of the man observaton wndow. The fve quntles are descrbed n Table 3. Table 4 reports the proporton of users wth ncreased average daly postng rates and wth decreased average daly postng rates (after versus before the nterventon) n each quntle. Treated users n the 2 nd quntle were sgnfcantly more lkely to ncrease ther postng rates compared to users n the control group (z = 2.42, p < 0.02), and margnally sgnfcantly less lkely to decrease ther postng rates compared to users n the control group (z = -1.85, p = 0.06). Users n the 4 th quntle, however, show the opposte result: treated users n that group 20 We also ran parametrc logstc regressons where the DV was whether the user ncreased (respectvely, decreased) ther postng rate after vs. before the nterventon, and the IVs ncluded a dummy for treatment, log (1+ number of followers on day 1), log(1+number of followers on day 1) 2, the nteracton between the treatment dummy and log (1+ number of followers on day 1), and the nteracton between the treatment dummy and log (1+ number of followers on day 1) 2. Comparable fgures were obtaned, although these parametrc regressons do not seem to capture the relatonshp between the number of followers and the mpact of the nterventon as well as the nonparametrc ones. Detals are avalable from the authors.

22 22 were sgnfcantly less lkely to ncrease ther postng rates compared to users n the control group (z = -2.18, p < 0.03), and sgnfcantly more lkely to decrease ther postng rates compared to users n the control group (z = 1.94, p = 0.05). The dfferences n the other quntles are not statstcally sgnfcant. Therefore, our results suggest that exogenously addng followers (or n the case of protected accounts, follow requests) made some users post more (users n the 2 nd quntle), made some users post less (users n the 4 th quntle), and had lttle effect on the others. 21 [ Insert Tables 3-4 Here ] The fact that the treatment had no effect on the 1 st and 5 th quntles s not surprsng. The 1 st quntle s composed of users wth very few followers who are only margnally actve and may hardly vst the Twtter platform. The 5 th quntle s composed of users wth over one thousand followers on average, for whom the addton of up to 100 followers over 50 days may have gone largely unnotced. The results n the 2 nd quntle are consstent wth ntrnsc utlty. As dscussed above, ntrnsc utlty from postng content should lead users to post more content on average followng our nterventon. The results n the 4 th quntle, however, are consstent wth magerelated utlty. As argued above, f the benefts from postng comes from attractng addtonal followers and f addtonal followers provde dmnshng margnal utlty, we should expect postng actvty to be reduced on average followng our nterventon. The results n the 3 rd quntle are consstent wth the effects of the two sources of utlty cancelng each other on average for users wth an ntal number of followers wthn the correspondng range. 21 Smlar results are obtaned when runnng a logstc regresson where the DV s whether each user ncreased (respectvely, decreased) ther postng rate after vs. before the nterventon, and the IVs nclude a dummy for treatment, dummes for the varous quntles, and nteractons between the treatment and quntle dummes. Results are avalable from the authors.

23 23 It s very common for companes to follow consumers on Twtter, partly n the hope that these consumers wll become advocates and contrbute content related to ther brand. Our modelfree analyss has manageral mplcatons related to ths practce. Indeed, based on our results t appears that followng consumers on Twtter may have the counterntutve effect of makng them less actve and therefore less lkely to contrbute content related to the brand. Ths s partcularly true for consumers wth relatvely large numbers of followers, who are precsely the ones typcally targeted by companes for ther ablty to reach more people wth brand-related messages (e.g., Goldenberg et al. 2009). In Secton 5 we present a complementary model-based analyss that provdes addtonal manageral nsghts by quantfyng ntrnsc and mage-related utlty and makng counterfactual predctons regardng the evoluton of Twtter Ecologcal valdty: perceved realsm of synthetc followers One potental concern wth our results s that our synthetc followers may have been more lkely to be perceved by treated users as beng fake, whch may have led treated users n our experment to react to the addton of followers (or follow requests n the case of protected accounts) dfferently than they would have normally. We addressed ths concern as follows. In Aprl 2012, we created a snapshot mage for the profle of each synthetc user, each treated user, and one randomly selected follower of each treated user who dd not have a protected account at that tme (76 treated users were n that case). 22 The mage was a screenshot from the profle summary publcly avalable on Twtter whch contaned the user s name, pcture, number of tweets to date, number of users followed and followng, and the three most recent tweets by the 22 By the tme we ran ths study, 5 of our treated users dd not exst anymore. Also, t was not possble for us to dentfy the followers of users wth protected accounts.

24 24 user (wth the excepton of protected accounts for whom recent tweets are not publcly avalable). Three hundred ffty-fve members of the Amazon Mechancal Turk panel, who were prescreened as havng Twtter accounts, assessed these profles based on the snapshot mages. Each respondent evaluated a random set of 20 profles n exchange for $1, and was asked to ndcate whether each profle seemed fake (a fake profle was defned as one that pretends to be another person or another entty n order to confuse or deceve other users ). By the end of the survey, each profle had receved an average of evaluatons. We computed the proporton of tmes each profle was judged to be fake. The mean (medan) of ths proporton was (0.192) among synthetc users, (0.192) among treated users, and (0.241) among the followers of treated users. The mean and medan proportons were not statstcally sgnfcantly dfferent between synthetc and treated users (p > 0.46). Followers of treated users, however, were sgnfcantly more lkely to be evaluated as fake compared to synthetc and treated users (all p < 0.01). Ths s probably because these users ncluded both commercal and non-commercal users. 23 In concluson, ths survey suggests that our synthetc users were not perceved as beng more fake than other non-commercal users (our treated users), and were perceved as sgnfcantly less fake than a random subset of the followers of non-commercal users. Ths suggests that our treatment has good ecologcal valdty. 23 The statstcal analyss reported here s based on the pont estmates of the probablty that each profle s judged to be faked. As an alternatve approach, we used a parametrc bootstrappng approach to construct confdence ntervals around the mean and medan probabltes reported n the text. We consdered a model n whch the posteror dstrbuton of the probablty that profle wll be judged to be fake s gven by: p ~Beta(0.01+f, 0.01+nf ), where f (respectvely nf ) s the number of tmes the profle was evaluated as fake (respectvely, non fake) n the data, and where the beta dstrbuton follows from an unnformatve pror (beta(0.01,0.01)) and bnomal lkelhood. We constructed confdence ntervals by drawng 10,000 values of p for each profle. Identcal conclusons were reached.

25 Impact of protected accounts As mentoned above, whle our nterventon nvolved addng new followers to publc accounts, for protected accounts t nvolved sendng follow requests that the treated users could ether accept or reject. The fact that protected accounts had the ablty to reject these requests may have reduced the mpact of our nterventon, whch should make the sgnfcant treatment effects found n quntles 2 and 4 more conservatve, and could artfcally attenuate the effect of the treatment n the other quntles. We recorded (manually) whch treated users had protected accounts at the tme of the nterventon (unfortunately we do not have ths nformaton for the nontreated users). Ffteen of our treated users were protected at the tme of the experment. These users were equally spread across the frst four quntles reported n Table 3 (4,4,4, and 3 protected users n quntles 1, 2, 3, and 4 respectvely, none n quntle 5). Therefore the null treatment effects n quntles 1, 3, and 5 appear unlkely to have been drven by a larger proporton of protected accounts n these quntles. Moreover, the results n Table 4 do not change qualtatvely when lmted to the publc treated users. Detals are avalable from the authors Alternatve explanaton One alternatve explanaton for the effect of our treatment on the 4 th quntle (those users who decreased ther postng rate after the nterventon) s that some users feel comfortable postng on Twtter when ther followers are lmted to mmedate relatons and when some level of ntmacy s preserved, but become less comfortable as ther posts become more publc. Ths would drve these users to contrbute less content after recevng addtonal, unknown followers. In order to nvestgate ths ssue, we were able to download, n Aprl 2012, the text of all tweets posted by 44 of our treated users durng the pre-treatment (days 1 to 56) and post-

26 26 treatment (days 107 to 160) perods of our man observaton wndow. 24 These 44 users posted 3,580 tweets n total durng these perods. We asked 749 members of the Amazon Mechancal Turk panel (prescreened to be Twtter users) to classfy these tweets, n exchange for $2. Each respondent was shown the text of 60 tweets randomly selected from the full set (no nformaton about each tweet other than ts text was provded) and was asked to answer the followng queston about each tweet: Is ths tweet meant for the user s close frends and famly members only? The response categores were Yes, No, and I do not know / I do not understand ths tweet. Each tweet was classfed by an average of respondents. We compute the proporton of occurrence of each response category for each tweet. Tables 5 to 7 report averages across users. We construct confdence ntervals usng a parametrc bootstrappng approach. 25 Table 5 reports the average across treated users, before and after the treatment. We see that the treatment decreased the proporton of prvate tweets slghtly, although the 95% confdence ntervals before and after the treatment overlap. Next, n order to nvestgate whether the treatment had a dfferent effect on users who were postng more prvate tweets before the treatment, we report n Table 6 the average categorzaton across treated users before the treatment, for those users who ncreased ther postng rate after the nterventon vs. those who decreased ther postng rate. We see that ndeed, treated users who decreased ther postng rate after the nterventon tended to post tweets that were more prvate before the 24 We were not able to retreve these data for treated users who dd not exst anymore as of Aprl 2012, who had protected accounts, and who had posted more than 3,200 tweets snce the end of our observaton wndow (due to lmts mposed by the Twtter API). The number of users for whom we have text data n each quntle (frst to ffth) s 7, 8, 8, 10, and We denote as pjk the multnomal probablty that a random evaluaton of tweet j by treated user would fall n response category k. We draw 10,000 random sets of probabltes for each tweet, accordng to: {p j1, p j2, p j3 }~Drchlet(0.01+ n j1, n j2, n j3 ), where n jk s the observed number of tmes tweet j by treated user was classfed n category k. Ths Drchlet dstrbuton results from an unnformatve pror (Drchlet(0.01, 0.01, 0.01)) combned wth the multnomal lkelhood functon.

27 27 treatment. Ths s consstent wth the hypothess that some users decreased ther postng rate after the treatment because ther audence changed from beng ntmate to beng more publc. However, n order for ths phenomenon to explan why treated users n the 4 th quntle posted less as a result of the nterventon, t would need to be the case that the tweets posted by these users before the treatment were relatvely more prvate. Table 7 reports the average categorzaton across treated users before the treatment, for users n the frst three quntles vs. the fourth vs. the ffth quntle (we group the frst three quntles n order to ncrease statstcal power and smlar conclusons are reached f the fve quntles are consdered separately). Users wth more followers at the begnnng of the observaton wndow tended to post tweets that were less prvate before the treatment: the dfference between the frst three quntles and the fourth quntle s statstcally sgnfcant, as well as the dfference between the fourth and the ffth quntles. Therefore f the effect of the treatment were solely drven by prvacy consderatons, we should expect treated users n the lower quntles, and not the fourth quntle, to be the ones decreasng ther postng rate after the treatment, and smlarly we should expect treated users n the hgher quntles, and not the second quntle, to be the ones ncreasng ther postng rate. In concluson, whle our analyss does provde support to the hypothess that users who use Twtter more prvately are more lkely to decrease ther postng rate after the addton of unknown followers, ths phenomenon does not appear to drve our results. [Insert Tables 5-7 Here] 5. Dynamc Dscrete Choce Model The results of the prevous model-free analyss are consstent wth the exstence of both ntrnsc and mage-related utlty among Twtter users, wth each source of motvaton beng more or less

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