Social Influence in Social Advertising: Evidence from Field Experiments (EC 2012)

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Social Influence in Social Advertising: Evidence from Field Experiments (EC 2012)

  1. 1. Social Influence inSocial AdvertisingEvidence from Field ExperimentsEytan Bakshy, Dean Eckles, Rong Yan, Itamar RosennEC 2012 Valencia, SpainJune 6, 2012
  2. 2. on substantive sci- that the size of the ● mprising hundreds of 0.25 utational challenges. complex aggregation Fraction of total pageviews hines. As such, most 0.20 e MapReduce paral- is particularly well- 0.15 e of Web panel data , we explore three di- ● ed work in Section 2, 0.10usage changes as indi- ●We find that the heav-wice as much of their 0.05 ● ●pical individuals, and me on e-mail. In Sec- ia l es s ch ai l ta ed m am ar . Whereas this issue r E− M Po Se G alose with and without ci So ocus on disparities in already online, find- a particularly strong turn to the Web for Goel,Figure 1: Percent of time spent on the top five most 2012 *S. J.M. Hofman, M.I. Sirer, 2012. Who Does What on the Web. ICWSMFinally, in Section 5, popular categories.
  3. 3. What is Social Advertising?▪ Uses social networks to: ▪ Automatically target audiences ▪ Persuade users via social influence processes ▪ Generate WOM spread▪ Traditional affordances: ▪ Persistent ▪ Advertiser-generated creative ▪ Demographic & keyword-based targeting
  4. 4. Evidence for Social Influence? Connectedness of inviter network: another feature of 5. Structural Dynamics in Invitation the inviter network is how densely the inviters are con- Network nected to each other. Since many people did not share their friend lists in FB, we consider the game family 5.1. Collective Effect of Inviters network, generated through invitations sent to friends, as a partial projection of the FB social network. We Data So far we have focused our analysis on the individual AND NETWORKS INFLUENTIALS then also control for the total number of inviters to an inviter. Next we turn to the invitees, as they interact with potentially multiple inviters, as illustrated by the invitee, and test the relationship between connectedness FIGURE 11 Theory 453 The Dynamics of Viral Marketing 19 (CC: clustering coefficient) of inviters and likelihood of invitation network in Figure 6 and Figure 8 b). When INFLUENCE RESPONSE FUNCTIONS FOR (A) THE SIR significant (B) THE DETERMINISTIC THRESHOLD RULE adoption. For both games, we find MODEL AND positive users join a game after receiving invitations from mul- correlation between the CC of the inviters and the ac-ges in in-ualitative 0.025 0.06 tiple sources, we can attribute their conversion to a col- Probability of joining a community when k friends are already members 0.08 ceptance rate of the invitee (YL: ! = 0.21*, DL: ! = quantita- lective effort. We define the acceptance_rate to be the approach 0.05 0.14*), which indicates that a more strongly connected ratio of the number of families one invitee joins over all Probability of Buying Probability of Buying 0.02 0.06 [22]; we 0.04 game network is more attractive for others to join. Con- its mem- distinct families that user was invited to join. We fur- 0.015 sidering the above result, we suspect that social games ther examine how a structured group of inviters exerts probabilityatistically 0.03 0.04 n DBLP, might be different from other diffusion agents as their 0.01 collective influence over a common invitee. between 0.02 utility is primarily in their social function: the utility 0.02 0.005 0.01 might be significantly amplified by the social structure Figure 11 shows that users are more likely to join as it accommodates, rather than its inherent utility as ay of work raph (see 0 0 0 2 5 4 6 they receive invitations20from more inviters. For both 8 10 10 15 0 1020 25 30 4030 50 60 35 40 game. In the next section, we will discuss how the so- 45 50eferred to YL and DL, the marginal rate is declining and the ac- Incoming Recommendations Incoming Recommendations k cial utility is exerted as one kind of network externality. The probability p of joining a2006 Backstrom et al. LiveJournal commu- Leskovec et al. 2007 different (a) Books (b) DVD s cluster- Figure 1: ceptance_rate becomes more saturated. nity as a function 0.2 the number of friends k already in the of 0.2ork based the com- community. Error bars represent two standard errors. 5.2. Network Effect of Families in Game NOTE.—Each function reflects the probability of choosing alternative B as a function of the number ni or as a fraction bi of others choosing B, respectively. and seek small assets Watts & Dodds. 2007 Probability of Buying Probability of Buying 0.15 0.15 0.20grow and threshold model but rather we investigated the pointwise features of invita-as early adopters, when networks are suf large assets Above that average individuals trigger an important role rate of adoptionnities” of Probability of joining a conference when k coauthors are already ’members’ of that conferencekeyword, 0.1 larger ones. tions. We now look at how the adoption takes place not as initiators. Finally, group structure ficiently sparse, but 0.1 0.1 Another qualitative difference between the SIR and appears to generally impede the effectiveness of influentialssubgraph 0.08 from a dynamic perspective: how invitationas initiators and early adopters. threshold models is that early adopters are consistently more both efficiencyre. Such varies across time, or different re- stages of the diffusion 0.10 xplicitly- 0.05 0.05 influential than average in both low- and high-density we study 0.06 gimes, as shown in figure 13 (again, for low- and high- whether there is a DISCUSSION network. First we are interested in probability different variance networks network externality effect in the game contagion in a in the top row and the bottom row, re- 0 0.04 0Watts 4& Dodds 2007 12 14 spectively). The reason, once again, is that more influential ining ap- 1 2 3 4 5 6 7 8 2 6 8 10 16 local community. Family membership can yieldthese results should be regarded as undermining Whether both Not so fast.. 0.00 Incoming Recommendations Incoming Recommendations pers; see individuals are more, not less, susceptible to influence them- what we have called the influentials hypothesis or as supe focused 0 50.02 (c) Music 10 15 (d) Video selves; thus, there explicit utility between influence and is no trade-off such as battle collaboration and it is ultimately an empirical question. Our main porting implicit structure number of neighbors (k) Figure 11 Acceptance rate over different number of benefit by imparting a might expect belonging” to play- so much that the influentials hypothesis influenceability. Given this difference, one “feeling of point, in fact, is not Figure 8: Probability of buying a book (DVD) given a numberby incoming recommendations. invitations received of one user that, in the SIR model, the fact, we observe that family membership leadsor wrong but that its microfoundations, by ers. In population of early adopters will is either right to 0ms. Stud- 0 2 4 6 8 10 12 14 16 18Usenet [4, Bakshy et al. 2009 Wei et al. 2010 k include more influentials. As figure 13 shows, thishigher commitment to the details of who influences whom and these on- better performance and intuition which we mean the Figure 2: The probability p of joining a DBLP community as a is not supported for the case of hypothesize these different kinds require very careful articulation in order for its validity “ordinary” influentials (top how, of utilities are quite Heterogeneity of inviter networks: we are interested function of the number of friends k already in the community. game. We row) but is supported for hyperinfluentials when the cor- to be meaningfully assessed. Whether stated explicitly o y related Error bars represent two standard errors. incoming recommendations on a particular a heterogeneous group 30 incoming rec- whether book. The maximum was of recommenders exerts integrate and accumulate as a network externality as the the effect that influentials are importan responding network is also sufficiently sparse: the first two not, any claim to he social we cut-off the plot over an individual than a more homo- of early adoptersgetsfigure 13C are clearly ommendations. For these reasonshigher influence when the number of observations “generations” family size in larger. necessarily makes a number of assumptions regarding the
  5. 5. Correlated Behavior, without Social Influence Unknown correlation between friends’ characteristics (expected to be stronger for closer friends)Known characteristics Xi Ui Xj Uj Unknown characteristics Yja(t0) Friend’s behavior Dija Ego’s behavior Yia(t1) i: ego j: friend a: stimulus
  6. 6. Correlated Behavior, with Social Influence Unknown correlation between friends’ characteristics (expected to be stronger for closer friends)Known characteristics Xi Ui Xj Uj Unknown characteristics Yja(t0) Friend’s behavior Social influence Dija Ego’s behavior Yia(t1) i: ego j: friend a: stimulus
  7. 7. Social Influence via Cues Unknown correlation between friends’ characteristics (expected to be stronger for closer friends)Known characteristics Xi Ui Xj Uj Unknown characteristics Influence via other Yja(t0) Friend’s behavior mechanisms Dija Presence of social cues Influence via social cues Ego’s behavior Yia(t1) i: ego j: friend a: stimulus
  8. 8. Outline▪ Experiment 1: Effect of multiple peers in WOM-type sponsored story ▪ What is the marginal increase in response rates due to additional social cues?▪ Experiment 2: Effect of a minimal cue ▪ How strong of a stimulus is required to influence users? ▪ Are certain friends more predictive than others? ▪ Are certain friends more influential than others?
  9. 9. Experiment 1: Influence of Multiple Peers▪ What is the true shape of the dose-response function for WOM-like sponsored story ad units? ▪ Users with Z peers are randomly assigned to see between 1 and Z peers ▪ Assignment and ordering is deterministic on (userid, adid) Zia = 3 Dia = 1 Dia = 2 Dia = 3
  10. 10. Experiment 1: Data▪ Population: random sample of users exposed to sponsored story ad units ▪ 23 million users ▪ 148 thousand ads ▪ 101 million distinct user-ad pairs▪ Outcomes ▪ Click-through rate ▪ “Like” rate
  11. 11. Naive Observational Estimate of Influence D=Z 2.0 1.8 normalized like rate 1.6 1.4 1.2 1.0 0.8 1 2 3 number of affiliated peers (Z)
  12. 12. Experimental Estimate of Influence Z=3 2.0 1.8 normalized like rate 1.6 1.4 1.2 1.0 0.8 1 2 3 number of cues shown (D)* all differences are significant with p < 0.005
  13. 13. Average Cue-Response Function for E1 Z=1 Z=2 Z=3 Z=1 Z=2 Z=3 1.5 2.0 normalized click rate normalized like rate 1.4 1.8 1.3 1.6 1.2 1.4 1.1 1.2 1.0 1.0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 number of peers shown (D) number of peers shown (D)* all within panel differences are significant with p < 0.005
  14. 14. Average Cue-Response Function for E1 Z=1 Z=2 Z=3 Z=1 Z=2 Z=3 1.5 2.0 normalized click rate normalized like rate 1.4 1.8 1.3 1.6 8.0% 8.9% 1.2 10.3% [5.7, 10.3] 1.4 10.5% [6.0, 12.1] [8.7, 11.9] [8.4, 12.4] 1.1 1.2 1.0 1.0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 number of peers shown (D) number of peers shown (D)* all within panel differences are significant with p < 0.005
  15. 15. Shortcomings of Experiment 1▪ Treatment is very strong (ad unit consists of social cues)▪ No “baseline” without social context▪ Does not tell us about how influence varies between users▪ Practical concern: ▪ Many advertisers want to be able to control the message
  16. 16. Experiment 2: Effect of a Minimal Cue▪ Users with Z peers are randomly assigned to see 0 or 1 peer▪ Assignment and ordering is deterministic on (userid, adid) Zia > 0 Dia = 0 Dia = 1
  17. 17. Experiment 2: Data▪ Population: random sample of users seeing social ad units ▪ 5.7 million users ▪ 1.1 million ads ▪ 137.5 million distinct user-ad pairs▪ Outcomes ▪ Click-through rate ▪ “Like” rate
  18. 18. Average Cue-Response Function for E2 Z=1 Z=2 Z=3 Z=1 Z=2 Z=3 normalized click rate normalized like rate 1.20 1.3 1.15 1.2 1.10 1.1 1.05 1.00 1.0 0 1 0 1 0 1 0 1 0 1 0 1 number of peers shown (D) number of peers shown (D)* all within-panel differences are significant with p < 0.005
  19. 19. Average Relative Increase due to Social Cue 1.20 1.20 1.15 1.15click risk ratio like risk ratio 1.10 1.10 1.05 1.05 1.00 1.00 1 2 3 1 2 3 number of affiliated peers (Z) number of affiliated peers (Z)
  20. 20. Effect of Tie Strength▪ How does influence depend on the relationship between the ego and their alter?▪ Approach: ▪ Consider subpopulation of E2 with Z=1 ▪ Measure tie strength as the fraction of communication (messages+comments) the ego directs toward the alter ▪ Estimate how responses vary with tie strength & treatment status
  21. 21. Estimated (average) response as a function of tie strength 1.6 D=1 normalized click rate normalized like rate 1.4 .3.2. Model. In order to pool information across similar values of tie strength and to 1.3 1.4ilitate statistical inference, we model responses to ads using logistic regression with ural splines. We fit a model in which ad clicks are predicted by the presence of a 1.2 1.2 D=0nimal social cue Dia for user i and ad a, measured tie strength Wij for user i with 1.1 affiliated peer j, and the user’s percentile-transformed total communication count 1.0 1.0 i• ). This model includes interactions of the minimal social cue with tie strength andh total communication count. 0.00 0.01 0.02 0.03 0.04 0.00 0.01 0.02 0.03 0.04 tie strength tie strengthn particular, the model is specified as Yija ⇠ ↵ + Dia + ⌧ f (Wij ) + ⌘Dia ·f (Wij ) + q(Ci• ) + q(Ci• )·f (Wij ) (1)ere f is a natural spline basis expansion for measured tie strength with knots at secondclick or like and third quartiles of measured tie strength overcommunication activity We fit the tie strength total all impressions.me model to the data for the response of liking the page.8function natural cubic spline quantile .3.3. Results. Response rates increase with tie strength both in the presence and presence of cue ence of social cues. Figure 7 shows predictedad i: subject j: alter a: response rates for user–ad pairs as 9
  22. 22. Social Cues Have a Stronger Effect forStrong Ties 1.4 1.20click risk ratio like risk ratio 1.3 1.15 1.2 1.10 1.05 1.1 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.00 0.01 0.02 0.03 0.04 0.05 0.06 tie strength tie strength
  23. 23. Recap▪ We explicitly examine the mechanism of influence via social cues ▪ Separate from exposure and targeting▪ We identify the average cue-response function ▪ Naive observational estimates of social influence massively overestimate peer effects ▪ Even the most minimal of social cues can generate substantial influence effects (e.g. +32% likes)▪ Ad behavior is more correlated for strong ties▪ Social cues have a stronger effect among strong ties
  24. 24. Implications & Future Work▪ Ad optimization: ▪ Correlation between user behaviors can be used to deliver more relevant ads▪ Observational studies should be taken with a healthy dose of skepticism▪ Future work: ▪ Are strong ties actually more influential? ▪ Individual differences in persuasion ▪ Other domains ▪ Other dependent variables
  25. 25. Thanks!▪ Coauthors: Dean Eckles, Rong Yan, Itamar Rosenn▪ Questions?
  26. 26. Other slides 1.30 1.25 1.20 click rate (%) 1.15 1.10 1.05 1.00 1 2 3 4 5 6 number of affiliated peers
  27. 27. The Confounding▪ Homophily: “birds of a feather flock together” ▪ Connected individuals are similar ▪ Robust across social networks▪ Homophily confounds the measurement of influence: ▪ Individuals’ future behavior mimics their friends because they are similar ▪ Increase in dose-response function may be due to homophily rather than friends

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