Network Research @fb (July 21, 2012)

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Network Research @fb (July 21, 2012)

  1. 1. Network Research @fb Field experiments and applications Eytan Bakshy Facebook Data Science Research @fb Faculty Summit July 31, 2012Wednesday, August 1, 12
  2. 2. Overview ▪ How do social networks influence user behavior? ▪ What is a reasonable model of influence? ▪ Information diffusion ▪ Bias in observational studies of contagion ▪ Effect of social signals in advertising ▪ A causal understanding of user behavior lets us: ▪ Explain the product to humans ▪ Build more effective products ▪ Develop better models of realityWednesday, August 1, 12
  3. 3. Motivation: What is a reasonable model of influence on Facebook?Wednesday, August 1, 12
  4. 4. Epidemic Modeling ▪ Standard model examines the spread of a single contagion with a constant rate of infection ▪ Assumes all individuals are equally susceptibleWednesday, August 1, 12
  5. 5. Epidemic Modeling ▪ Standard model examines the spread of a single contagion with a constant rate of infection ▪ Assumes all individuals are equally susceptible ▪ Focus on threshold at which global outbreaks occur R ≥ β/γWednesday, August 1, 12
  6. 6. ! ! Epidemic Modeling *+!* *+!1 ! ! ! -.4 -.3 ! *+!0 -.2 &"()"*+, ! ! ▪ Standard model examines the %$&"() ! *+!/ -.1 ! ! spread of a single contagion ! ! *+!. -.0 ! ! with a constant rate of ! !- ! / *+ -. ! ! infection !, -.- ! *+ ! ! *++ *+* *+1 *+0 *+/ . / 1 3 ▪ Assumes all individuals are where the !"#$ !"#$% left (right) equally susceptible ! (a) Cascade Sizes isfied (violated). Depth (b) Cascade Leaf ! *+!* -.4 measured by (log) mea ▪ Focus on threshold at which partition. Thus, for e ! *+!1 -.3 ! ! ! Figure 4: (a). Frequency distribution of upwar that users with casc global outbreaks occur !0 2 &"()"*+, ! ! *+ -. sizes. (b). Distribution of cascade depths. by dir age 6.2 reposts %$&"() ! *+!/ -.1 ! ! ! predicted to have the ▪ Information on the Web rarely *+!. -.0 ! ! ! ating cascades of appr ! appears to go “viral” !, !- we study size or depth, therefore, the implication the Unsurprisingly, is ! / *+ -. ! ! *+ most-.- events do not spread at all, and even moderately s ! provides the most info ! ! ! ! cascades are extremely rare. the local, not the tot *++ *+* *+1 *+0 *+/ . / 1 3 5 !"#$ To identify consistently influential individuals, we ag !"#$% this is likely due to th gated all URL posts by user and computed individual- are of depth 1, so that (a) Cascade Sizes (b)as the logarithm of the average size of all casc Cascade Depths influence Hofman, Mason, Watts 2011 dictor of total adoptio Bakshy, for which that user was a seed. followers is an inform We then fit a regres Figure 4: (a). Frequency distribution of cascade tree model [6], in which a greedy optimization process re are the only two featu sizes. (b). Distribution of sively partitions the feature space,abling us toin a piecew cascade depths. resulting visualize iWednesday, August 1, 12
  7. 7. Threshold Models of Social Contagion ▪ Threshold models: become activated after k contacts are activated ▪ Not clear that local consensus factors into individual decisions in sharing contentWednesday, August 1, 12
  8. 8. Threshold Models of Social Contagion ▪ Threshold models: become activated after k contacts are activated ▪ Not clear that local consensus factors into individual decisions in sharing content ▪ Positive externalities: e.g. adoption of a technology ▪ Utility of many activities do not strongly depend on number of contactsWednesday, August 1, 12
  9. 9. 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 5 0.02 (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 using” in becomes too small and the error geneouslarge. One might argue that if a user is receiv- bars too group. influentials. Thus, although hyperinfluentials do not appear nature of interpersonal influence, the structure of influence ofWednesday, August 1, 12 to create new posts in that community and the standard errors of the estimates be disproportionately effective in triggering large cas- how a one typically gains the right We calculate the purchase probabilities to We use the time interval between when the nth and what is meant by “important.” Regard networks, and even
  10. 10. 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: stimulusWednesday, August 1, 12
  11. 11. 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: stimulusWednesday, August 1, 12
  12. 12. Social Influence via Social 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: stimulusWednesday, August 1, 12
  13. 13. The Role of Social Networks in Information Diffusion E. Bakshy, I. Rosenn, C.A. Marlow, L.A. Adamic ACM WWW 2012Wednesday, August 1, 12
  14. 14. Information Diffusion: Outline ▪ Field experiment to tests causal questions: ▪ To what extent does feed increase sharing? ▪ How is tie strength predictive of: ▪ Influence on social media ▪ Sources of external correlation? ▪ What is the role of weak ties in disseminating information?Wednesday, August 1, 12
  15. 15. Social Influence via News Feed 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 sharing behavior mechanisms Dija Facebook news feed Influence via news feed Ego’s sharing behavior Yia(t1) i: ego j: friend a: URLWednesday, August 1, 12
  16. 16. Experimental Design & Approach ▪ URLs are randomly held out from users’ news feed at the time of display ▪ (viewer, URL) pairs are deterministically assigned into the feed and no feed condition ▪ Evaluation: ▪ Compare the likelihood of sharing in each condition ▪ Examine effect of feed as a function of tie strengthWednesday, August 1, 12
  17. 17. Data ▪ Random sample of all (user, URL) pairs eligible to be shown in the Facebook news feed between a 7 week period in 2010 ▪ 253,238,367 subjects ▪ 75,888,466 URLs ▪ 1,168,633,941 distinct subject-URL pairs (random trials)Wednesday, August 1, 12
  18. 18. What is the Overall Effect of Feed on Sharing? ▪ Two methods for comparing probabilities: ▪ Average treatment effect (on the treated): pfeed - pno feed ▪ Relative risk ratio: pfeed / pno feed ▪ Users were 7.3x (95% CI=[7.2, 7.7]) times more likely to share in the feed condition compared to no feed ▪ 0.260% of stories displayed in feed condition were shared ▪ 0.044% of stories held out in no feed condition were sharedWednesday, August 1, 12
  19. 19. How Does Sharing Increase with Number of Friends? 0.030 condition 0.030 10 probability of sharing 0.025 feed 0.025 8 no feed p f eed − p no feed 0.020 0.020 p f eed p no feed 6 0.015 0.015 4 0.010 0.010 0.005 0.005 2 0.000 0.000 0 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 number of sharing friends number of sharing friends number of sharing friendsWednesday, August 1, 12
  20. 20. Strong Ties are Individually More Influential ▪ Users are more likely to share the same content as their strong ties - even if they didn’t see the content in their feed ▪ Users are more likely to share content from strong ties 0.008 0.008 0.008 probability of sharing probability of sharing probability of sharing condition feed 0.006 0.006 0.006 no feed 0.004 0.004 0.004 0.002 0.002 0.002 0.000 0.000 0.000 0 2 4 6 8 10 12 0 1 2 3 4 5 6 7 0 1 2 3 4 comments received messages received photo coincidencesWednesday, August 1, 12
  21. 21. Weak Ties Expose Users to Novel Information ▪ Users are many times more likely to share content via exposure from weak ties ▪ Signals from strong ties are more likely to be redundant with external activity 10 10 10 8 8 8 p f eed p no feed p f eed p no feed p f eed p no feed 6 6 6 4 4 4 2 2 2 0 0 0 0 2 4 6 8 10 12 0 1 2 3 4 5 6 7 0 1 2 3 4 comments received messages received photo coincidencesWednesday, August 1, 12
  22. 22. Collective Influence of Ties ▪ Strong ties have a higher likelihood of causing their friends to share ▪ However, most ties are weak ties ▪ Total influence can be computed as: ▪ Infweak = ATET(ts=0) * number of weak ties ▪ Infstrong = ATET(ts>0) * number of strong ties cumulative fraction of ties in feed 0.98 0.96 0.94 type 0.92 comments received messages received 0.90 photo coincidences thread coincidences 0 10 20 30 40 50 tie strengthWednesday, August 1, 12
  23. 23. Weak Ties are Collectively More Influential comments weak strong messages weak tie strength strong weak photos strong weak threads strong 0 20 40 60 80 % influence on feedWednesday, August 1, 12
  24. 24. Bias in Observational Studies of Social Contagion D. Eckles & E. Bakshy Working paperWednesday, August 1, 12
  25. 25. Motivation & Design ▪ In most situations one only has observational data ▪ How much does this bias estimates of social contagion? ▪ Approach ▪ Use results from feed experiment as gold standard ▪ Construct “control” group using non-experimental data ▪ Compare how non-experimental control performs relative to gold standardWednesday, August 1, 12
  26. 26. Observational estimators of p(0) ▪ Naïve estimate: ▪ Pick random users and URLs in proportion to URL’s abundance in feed ▪ Compute the proportion of users who share URL in the future ▪ Estimate of p(0) is average weighted by number of exposed pairs from each domain ▪ Propensity score stratification: ▪ Estimate probability of exposure via feed (propensity score) of random (user, url) pairs for each domain using logistic regression ▪ Compute percentiles of propensity scores for each domain ▪ Compute proportion of pairs resulting in sharing in each percentile ▪ Estimate of p(0) is average weighted by number of exposed pairsWednesday, August 1, 12
  27. 27. which models include the corresponding variables as predictors. Propensity Category Demographics Name Age Gender Description As indicated on profile As indicated on profile Models A, B, Bs , D A, B, Bs , D Models Relationship status Political a liation As indicated on profile As indicated on profile A, D A, D Facebook Friend count Number of friendships at start of A, B, Bs study Initiation count and Number and proportion of extant A prop. friendships initiated Tenure Days since registration of account A, B, Bs Profile picture Whether the user has a profile pic- A ▪ A – all Visitation freq. ture Days active in prior 30 day period A, B, Bs Days active in prior 91 and 182 day A ▪ B – selection using periods domain expertise Communication Action count Number of posts (including URLs), comments, and likes in a one month A period ▪ D – demographic Post count Number of posts (including URLs) A in a one month period ▪ S – general Comment count Number of comments on posts in a A, B, Bs one month period sharing behaviors Like count Number of posts and comments A “liked” in a one month period ▪ Bs, Ss – Add same URL sharing Shares Number of URLs shared in a one month period A, B, Bs , S, Ss domain sharing Unique domains Number of unique domains of URLs A, B, Bs , S, Ss shared in a six month period Same domain shares Number of URLs shared in a six A, Bs , Ss month period with the same domain as outcomeWednesday, August 1, 12
  28. 28. exp ● naive ●1 | M = 1), D ● S ● B ● Ss ● Bs ●1) (0) p . A ● (4.3) 5 10 15 20 25 30 35m = 1. We then compare observational Risk ratioities, such as the risk ratio, p(1) /p(0) , toWednesday, August 1, 12
  29. 29. CHAPTER 4. EXPERIMENTAL EVALUATION 46 D ● is also called the (causal) risk di↵erence. For each estimator ATETk , we can char- acterize these discrepancies in multiple ways. Most simply, we can compute the S ● discrepancy on the probability scale; that is, the error in risk di↵erence: B ● ⇣ ⌘ ATETk ATETexp = pk ˆ (1) (0) pk ˆ p(1) p(0) . ˆexp ˆexp Ss ● To state the size of this discrepancy in a way that account for the size of the exper- Bs imental estimate, we also report the relative error in ● di↵erence risk A ATETk ATETexp ● . exp | |ATET 0 20 40 60 80 100 Finally, since the error of the naive estimate can be very large compared to the Percent error reduction for risk difference experimental estimate itself, we also use a measure of bias reduction: the percent error change from the naive estimate, ! |ATETk ATETexp | 100 1 . naive ATETexp | |ATET In some additional comparisons, we also use an di↵erent estimate as the baseline for this measure of bias reduction.Wednesday, August 1, 12 For much of the analysis, we take the experimental estimates as the gold standard
  30. 30. Bias reduction is greater for popular domains S D 100 80 60 Percent error reduction for risk difference 40 20 ● ● ● ● ● ● ● ● ● 0 ● ● ● Ss B ● 100 ● 80 60 ● 40 ● ● ● ● 20 ● ● ● ● ● 0 A Bs 100 ● ● ● 80 ● 60 40 ● ● 20 ● ● ● ● ● ● 0 [0, .1] (.1, .25] (.25, .5] (.5, .75] (.75, .9] (.9, 1] [0, .1] (.1, .25] (.25, .5] (.5, .75] (.75, .9] (.9, 1] Quantiles of number of treated pairs per domainWednesday, August 1, 12
  31. 31. Takeaways ▪ Demographics, activity level, etc provide poor controls ▪ The most powerful control is relevant past behavior ▪ Propensity score stratification yields substantial bias reduction ▪ Trying to control for contagion is problematic when no past behavior is availableWednesday, August 1, 12
  32. 32. Social Influence in Social Advertising: Evidence from Field Experiments E. Bakshy, D. Eckles, R. Yan, I. Rosenn ACM EC 2012Wednesday, August 1, 12
  33. 33. Context: Social Advertising ▪ Uses the social graph to: ▪ Automatically target audiences ▪ Persuade users via social influence processes ▪ Generate viral diffusion ▪ Can include traditional affordances: ▪ Persistent ▪ Advertiser-generated creative ▪ Demographic & keyword-based targetingWednesday, August 1, 12
  34. 34. Social Influence via Social Cues in Ads 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 page liking behavior mechanisms Dija Presence of social cues Influence via social cues Page like Yia(t1) i: ego j: friend a: stimulusWednesday, August 1, 12
  35. 35. Social Influence in Social Ads: 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?Wednesday, August 1, 12
  36. 36. 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 = 3Wednesday, August 1, 12
  37. 37. 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” rateWednesday, August 1, 12
  38. 38. 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)Wednesday, August 1, 12
  39. 39. 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 < 0.005Wednesday, August 1, 12
  40. 40. 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 < 0.005Wednesday, August 1, 12
  41. 41. 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 = 1Wednesday, August 1, 12
  42. 42. 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 rateWednesday, August 1, 12
  43. 43. 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) to the alter ▪ Estimate how responses vary with tie strength & treatment statusWednesday, August 1, 12
  44. 44. 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 asunction of 1,tie strength with (solid) and without (dashed) the minimal cue.9 Since Wednesday, August 12
  45. 45. Social Cues Have a Stronger Effect for Strong Ties 1.4 1.20 click 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 strengthWednesday, August 1, 12
  46. 46. Recap ▪ We explicitly examine the mechanism of influence via social cues ▪ Distinct from exposure and targeting ▪ We identify the average cue-response function ▪ Naive observational estimates of social influence massively overestimate peer effects ▪ Light grey text 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 tiesWednesday, August 1, 12
  47. 47. Implications & Future Work ▪ Applications: ▪ Correlation between user behaviors can be used to deliver more relevant ads ▪ Open problems: ▪ Are strong ties actually more influential? ▪ Individual differences in persuasion ▪ Are similar patterns seen in other online applications?Wednesday, August 1, 12
  48. 48. Summary of Network Research @fb ▪ Large-scale randomized field experiments can tease apart assumptions made by models ▪ Explicit analysis of causal mechanisms: ▪ Contribute to science ▪ Explain product to customers ▪ Surface opportunities for optimizationWednesday, August 1, 12
  49. 49. Thanks! ▪ Coauthors: ▪ Lada Adamic ▪ Dean Eckles ▪ Cameron Marlow ▪ Itamar Rosenn ▪ Rong Yan ▪ Questions?Wednesday, August 1, 12

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