Social Influence Bias: A Randomized Experiment
Lev Muchnik
Science 341
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Social Influence Bias: ment on true quality is nearly impossible in ob-servational data (21–27). For example, popularproducts may be popular because of the irrational A Randomized Experiment effect of past positive ratings, or alternatively, the on August 8, 2013 best products may become popular because theyare of the highest quality. We must distinguish Lev Muchnik,1 Sinan Aral,2* Sean J. Taylor3 these explanations to determine the extent towhich social influence creates irrational herding.
Our society is increasingly relying on the digitized, aggregated opinions of others to We therefore designed and analyzed a large- make decisions. We therefore designed and analyzed a large-scale randomized experiment scale randomized experiment to quantify the on a social news aggregation Web site to investigate whether knowledge of such aggregates effects of social influence on users' ratings and distorts decision-making. Prior ratings created significant bias in individual rating behavior, discourse on a social news aggregation Web and positive and negative social influences created asymmetric herding effects. Whereas site, where users contribute news articles and negative social influence inspired users to correct manipulated ratings, positive social discuss them. Users of the site that we studied influence increased the likelihood of positive ratings by 32% and created accumulating write comments in response to posted articles, positive herding that increased final ratings by 25% on average. This positive herding and other users can then "up-vote" or "down-vote" was topic-dependent and affected by whether individuals were viewing the opinions of friends these comments, yielding an aggregate current or enemies. A mixture of changing opinion and greater turnout under both manipulations rating for each posted comment equal to the num- together with a natural tendency to up-vote on the site combined to create the herding ber of up-votes minus the number of down-votes.
effects. Such findings will help interpret collective judgment accurately and avoid social Users do not observe the comment scores before influence bias in collective intelligence in the future.
clicking through to comments—each impression of a comment is always accompanied by that com- Werelyonratingscontributedbyothers Collective intelligence has recently been her- ment'scurrentscore,tyingthecommenttothe to make decisions about which hotels, alded as a harbinger of accelerated human po- score during users' evaluation—and comments books, movies, political candidates, tential (6). But, social influence on individuals' are not ordered by their popularity, mitigating news, comments, and stories are worth our time perceptions of quality and value could create selection bias on high (or low) rated comments.
and money (1). Given the widespread use and herding effects that lead to suboptimal market Similar scoring mechanisms are widely used on economic value of rating systems (2–4), it is im- outcomes (7, 8); rich-get-richer dynamics that the Web to reward users for supplying insightful portant to consider whether they can successfully exaggerate inequality (9–12); a group think men- or interesting analysis, while penalizing those post- harness the wisdom of crowds to accurately ag- tality that distorts the truth (13); and measurable ing irrelevant, redundant, or low-quality comments.
gregate individual information. Do they produce disruptions in the wisdom of crowds (14). If The vast majority of interuser relations occur on useful, unbiased, aggregate information about perceptions of quality are biased by social influ- the Web site, in contrast to Web sites whose mem- the quality of the item being rated? Or, as sug- ence, attempts to aggregate collective judgment bers also interact offline. The data therefore pro- gested by the experiments of Salganik et al. (5), and socialize choice could be easily manipulated, vide a unique opportunity to comprehensively are outcomes path dependent, yielding different with dramatic consequences for our markets, our study social influence bias in rating behavior.
aggregate ratings for items of equivalent quality? politics, and our health.
Over 5 months, 101,281 comments submitted The recent availability of population-scale on the site were randomly assigned to one of data sets on rating behavior and social commu- three treatment groups: up-treated, down-treated, 1School of Business Administration, The Hebrew University of nication enable novel investigations of social or control. Up-treated comments were artificially Jerusalem, Mount Scopus, Jerusalem, 91905 Israel. 2MIT Sloan School of Management, 100 Main Street, Cambridge, MA –20). Unfortunately, our under- given an up-vote (a +1 rating) upon the com- 20142, USA. 3NYU Stern School of Business, 44 West 4th standing of the impact of social influence on col- ment's creation, whereas down-treated comments Street, New York, NY 10012, USA.
lective judgment is limited because empirically were given a down-vote (a –1 rating) upon the *Corresponding author. E-mail: [email protected] distinguishing influence from uninfluenced agree- comment's creation. Users were unaware of the SCIENCE VOL 341 9 AUGUST 2013 Mean Score
Probability to Up-vote
Probability to Down-vote
Fig. 1. Effect of manipulation on voting behavior. The positively manipulated rater random effects. (C) The mean final scores of positively manipulated, nega- treatment group (up-treated), the negatively manipulated treatment group (down- tively manipulated, and control group comments are shown with 95% confidence treated), and the control group (dotted line) are shown. The probabilities to up-vote intervals inferred from Bayesian linear regression of the final comment score with (A) and down-vote (B) positively manipulated, negatively manipulated, and control commenter random effects. Final mean scores on this Web site are measured as group comments are shown by the first unique viewer; 95% confidence intervals the number of up-votes minus the number of down-votes. We discuss the impli- are inferred from Bayesian logistic regression with commenter, rater, and commenter- cations of this measurement in greater detail in the supplementary materials.
on August 8, 2013 manipulation and unable to trace votes to any Fig. 2. Effects of topic on particular user. As a result of the randomization, herding. Mean final scores comments in the control and treatment groups of positively manipulated and were identical in expectation along all dimen- control group comments are sions that could affect users' rating behavior shown with 95% confidence except for the current rating. This manipulation intervals inferred from Bayesian created a small random signal of positive or neg- linear regression of the fi- ative judgment by prior raters for randomly se- nal comment score with com- menter random effects across lected comments that have the same quality in the seven most active topic expectation, enabling estimates of the effects of categories on the site, or- social influence holding comment quality and all Mean Score
dered by the magnitude of other factors constant. The 101,281 experimental the difference between the comments (of which 4049 were positively treated mean final score of positive- and 1942 were negatively treated to reflect the ly manipulated comments natural proportions of up- and down-votes on and the mean final score of the site) were viewed more than 10 million times control comments in each and rated 308,515 times by subsequent users.
We may sample users multiple times, and nonrandom heterogeneity may exist in users'comment-generating process, their selection ofcomments to vote on, and in relationships be- tween commenters and raters. We therefore es-timated hierarchical Bayesian models of votingbehavior and specified commenter, rater, andcommenter-rater pair random effects; i.e., theconfidence intervals are based on repeated re- all users' ratings for comments in the three groups the control group (P = 1.0 × 10–6) (Fig. 1A). Up- sampling, creating a distribution of parameter treated comments were not down-voted signifi- estimates from which the 95% confidence bands Figure 1A shows the immediate up-vote cantly more or less frequently than the control are derived (see materials and methods in the and down-vote probabilities for the first viewer group, so users did not tend to correct the upward of comments in each of the three categories.
manipulation. In the absence of a correction, pos- We first compared the probabilities that com- Up-votes were 4.6 times as common as down- itive herding accumulated over time.
ments in each group would be up-voted or down- votes on this site, with 5.13% of all comments The positive manipulation created a posi- voted by the first viewer after the manipulation.
receiving an up-vote by the first viewer of the tive social influence bias that persisted over our These probabilities measure the immediate effect comment and only 0.82% of comments receiv- 5-month observation window, generating accu- of current ratings on users' rating behavior. We ing a down-vote by the first viewer. The up-vote mulating herding effects that increased comments' then analyzed comments' long-run ratings dis- treatment significantly increased the probabil- final mean ratings by 25% relative to the final tributions and final mean scores by aggregating ity of up-voting by the first viewer by 32% over mean ratings of control group comments (c2 test; 9 AUGUST 2013 VOL 341 SCIENCE categories with significant differences in control and treatment ratings and those with no significantdifferences had similar levels of activity. There wasno significant negative herding in any category.
Friendship also moderated the impact of social influence on rating behavior (Fig. 3, Aand B). The Web site has a feature wherebyusers can indicate that they "like" or "dislike"other users, forming "friends" and "enemies"social preference graphs. Unsurprisingly, friendsof the commenter were more likely to up-vote acomment than those who disliked him or her(9.2% versus 2.7%, c2 test; P = 2.7 × 10–49)[compare the average (dotted line) in Fig. 3A tothe average (dotted line) in Fig. 3B]. Friendsalso tended to herd on current positive ratings Fig. 3. Effects of friendship on rating behavior. The figure shows the probability of a friend (A) and (friends' probability to up-vote a positively ma- enemy (B) of the commenter to up-vote positively manipulated, negatively manipulated, and control group nipulated comment: 0.122 versus friends' prob- comments. Friends and enemies are defined as users who had previously clicked a button on the Web site ability to up-vote a control comment: 0.092; c2 labeling the commenter as someone they "liked" or "disliked," respectively.
test; P = 1.4 × 10–2) and to correct commentswith negatively manipulated ratings (friends'probability to up-vote a negatively manipulatedcomment: 0.176 versus friends' probability to up-vote a control comment: 0.092; c2 test; P =4.0 × 10–12) (Fig. 3A), mirroring the cooperationfound in human social networks (28). By con- on August 8, 2013 trast, enemies of the commenter were not sus-ceptible to social influence. Enemies' ratings wereunaffected by our treatments, possibly becauseof the small sample of potential first ratings byenemies (though there are a substantial numberof enemies in the community, they are less active)(Fig. 3B).
Finally, social influence in ratings behavior did not affect discourse in our setting during the5-month observation period. Neither the positive nor the negative manipulation affected the aver-age number of replies (Fig. 4A) or the averagedepth of the discussion tree created in response to Fig. 4. Effects on subsequent discourse. The figure displays the average number of responses (A) a comment (Fig. 4B).
and the average depth of the discussion tree (B) that developed in response to positively manipulated, Several data-generating processes could ex- negatively manipulated, and control group comments; 95% confidence intervals are inferred from plain our findings. A selection effect could in- Bayesian linear regressions with author random effects.
spire different populations of voters to turn out to rate the item (selective turnout)—for example, P = 2.3 × 10–11) (Fig. 1C), and Kolmogorov- 10–3) (Fig. 1B). However, this effect was offset if the negative manipulation inspired voters who Smirnov (K-S) tests showed that the final score by a larger correction effect. The probability that tend to down-vote (negative voters) to vote in distribution of up-treated comments was signif- a down-treated comment would subsequently be higher proportion. Alternatively, prior ratings icantly shifted toward higher scores (K-S test up-voted was 0.099, whereas the probability that could bias users' voting behavior by changing statistic: 0.083; P = 1.2 × 10–23). Comments in a control comment would be up-voted was sig- their opinions about comment quality and there- the up-treated group were also significantly more nificantly different at 0.054 (c2 test; P = 1.0 × fore their votes (opinion change). We analyzed likely than those in the control group to accu- 10–30) (Fig. 1A). This correction neutralized so- changes in turnout (the likelihood of rating rather mulate exceptionally high scores. Up-treated com- cial influence in the ratings of negatively manip- than just viewing comments) and changes in pos- ments were 30% more likely to reach or exceed ulated comments, and their final mean ratings itivity (the proportion of positive ratings) across a score of 10 (6.4% versus 4.9% in the control were not statistically different from the control subgroups in our study population to identify var- group, c2 test; P = 2.0 × 10–5). The small ma- group's final mean ratings (Fig. 1C).
iance in our results explained by turnout effects nipulation of a single random up-vote when the We next estimated changes in the final mean and opinion change, respectively. We divided comment was created resulted in significant- score for up-treated comments compared to con- raters and commenters, on the basis of their rating ly higher accumulated ratings due to social trol comments in the seven most active topic history, along four subgroup dimensions by their categories on the Web site. We found significant positivity (proportion of positive votes), the com- Positive and negative social influence created positive herding effects for comment ratings in menters' quality (prior scores of their comments), asymmetric herding effects. The probability of "politics," "culture and society," and "business," the frequency with which a rater rated a particular down-treated comments receiving subsequent but no detectable herding behavior for comments commenter, and whether raters were friends or down-votes was 0.014, whereas the probability in "economics," "IT," "fun," and "general news" enemies with commenters. We then compared treat- of control comments receiving a down-vote was (Fig. 2). These differences are not due to the fre- ment effects with expected voting behavior in 0.007; a significant difference (c2 test; P = 1.1 × quency of commenting in these categories, as these subgroups. Our analysis revealed several SCIENCE VOL 341 9 AUGUST 2013 Fig. 5. Effects on turnout versus positivity. The figure displays treatment effects of the negative and positive manipulation on turnout (the likelihood of rating) andpositivity (the proportion of positive votes) for negative(positive) raters, defined as active raters (with at least 100 votes on control comments) who display less than (greater than) the median proportion of positive votes on control comments. Results displayed in red are sta-tistically significant at the 95% level, whereas results displayed in gray are not. (A) Treatment effect of thenegative manipulation on positivity; (B) treatment effectof the positive manipulation on positivity; (C) treatment effect of the negative manipulation on turnout; and (D)treatment effect of the positive manipulation on turnout.
In each panel, the first estimate is the treatment effect on negative raters, the second estimate is the treatment effect on positive raters, and the third estimate is theratio of the treatment effects on negative/positive raters.
The treatment effects displayed are odds ratios of the treatment effect on the treated compared to the control Treatment Effect (odds ratio) for negative and positive raters. The negative manipu-lation created countervailing opinion change for positive and negative raters, increasing the positivity of negativeraters and decreasing the positivity of positive raters,whereas both treatments increased turnout uniformly across both subgroups.
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Web site, but code is available upon request. All of the user Pluripotent Stem Cells Induced ical substitutes of Oct4 in the absence of trans-genes. We found that VC6T plus FSK (VC6TF)induced some GFP-positive clusters expressing from Mouse Somatic Cells E-cadherin, a mesenchyme-to-epithelium transi-tion marker, reminiscent of early reprogramming by Small-Molecule Compounds by transcription factors (10, 11) (Fig. 1C and fig.
S3). However, the expression of Oct4 and Nanogwas not detectable, and their promoters remained Pingping Hou,1* Yanqin Li,1* Xu Zhang,1,2* Chun Liu,1,2* Jingyang Guan,1* Honggang Li,1* on August 8, 2013 hypermethylated, suggesting a repressed epige- Ting Zhao,1† Junqing Ye,1,2† Weifeng Yang,3† Kang Liu,1† Jian Ge,1,2† Jun Xu,1† Qiang Zhang,1,2† netic state (fig. S3).
Yang Zhao,1‡ Hongkui Deng1,2‡ To identify small molecules that facilitate late reprogramming, we used a doxycycline (DOX)– Pluripotent stem cells can be induced from somatic cells, providing an unlimited cell inducible Oct4 expression screening system, resource, with potential for studying disease and use in regenerative medicine. However, adding DOX only in the first 4 to 8 days (6). Small- genetic manipulation and technically challenging strategies such as nuclear transfer molecule hits, including several cAMP agonists used in reprogramming limit their clinical applications. Here, we show that pluripotent (FSK, Prostaglandin E2, and Rolipram) and epi- stem cells can be generated from mouse somatic cells at a frequency up to 0.2% using a genetic modulators [3-deazaneplanocin A (DZNep), combination of seven small-molecule compounds. The chemically induced pluripotent 5-Azacytidine, sodium butyrate, and RG108], were stem cells resemble embryonic stem cells in terms of their gene expression profiles, epigenetic identified in this screen (fig. S4 and table S1B).
status, and potential for differentiation and germline transmission. By using small molecules, To achieve complete chemical reprogramming exogenous "master genes" are dispensable for cell fate reprogramming. This chemical without the Oct4-inducible system, these small reprogramming strategy has potential use in generating functional desirable cell types molecules were further tested in the chemical re- for clinical applications.
programming of OG-MEFs without transgenes.
When DZNep was added 16 days after treatment Pluripotentstemcells,suchasembryonic ized. Moreover, their effects on inhibiting and withVC6TF(VC6TFZ),GFP-positivecellswere Downloaded from stem cells (ESCs), can self-renew and activating the function of specific proteins are obtained more frequently by a factor of up to 65 differentiate into all somatic cell types.
often reversible and can be finely tuned by varying than those treated with VC6TF, forming compact, Somatic cells can be reprogrammed to become the concentrations. Here, we identified small- epithelioid, GFP-positive colonies without clear- pluripotent via nuclear transfer into oocytes or molecule combinations that were able to drive the cut edges (Fig. 1, D and E, and fig. S5). In these through the ectopic expression of defined factors reprogramming of mouse somatic cells toward cells, the expression levels of most pluripotency (1–4). However, exogenous pluripotency-associated pluripotent cells.
marker genes were elevated but were still lower factors, especially Oct4, are indispensable for es- To identify small molecules that facilitate cell than in ESCs, suggesting an incomplete repro- tablishing pluripotency (5–7), and previous repro- reprogramming, we searched for small molecules gramming state (fig. S6). After switching to 2i- gramming strategies have raised concerns regarding that enable reprogramming in the absence of Oct4 medium with dual inhibition (2i) of glycogen the clinical applications (8, 9). Small molecules using Oct4 promoter-driven green fluorescent pro- synthase kinase-3 and mitogen-activated protein have advantages because they can be cell perme- tein (GFP) expression (OG) mouse embryonic kinase signaling after day 28 posttreatment, cer- able, nonimmunogenic, more cost-effective, and fibroblasts (MEFs), with viral expression of Sox2, tain GFP-positive colonies developed an ESC-like more easily synthesized, preserved, and standard- Klf4, and c-Myc. After screening up to 10,000 small morphology (domed, phase-bright, homogeneous molecules (table S1A), we identified Forskolin with clear-cut edges) (Fig. 1F) (12, 13). These col- (FSK), 2-methyl-5-hydroxytryptamine (2-Me- onies could be further cultured for more than 30 College of Life Sciences and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China. 2School 5HT), and D4476 (table S1B) as chemical "sub- passages, maintaining an ESC-like morphology of Chemical Biology and Biotechnology, Peking University stitutes" for Oct4 (Fig. 1, A and B, and figs. S1 (Fig. 1, G and H). We refer to these 2i-competent, Shenzhen Graduate School, Shenzhen 518055, China. 3Beijing and S2). Previously, we had developed a small- ESC-like, and GFP-positive cells as chemically Vitalstar Biotechnology Co., Ltd., Beijing 100012, China.
molecule combination "VC6T" [VPA, CHIR99021 induced pluripotent stem cells (CiPSCs).
*These authors contributed equally to this work.
(CHIR), 616452, Tranylcypromine], that enables Next, we optimized the dosages and treatment †These authors contributed equally to this work.
reprogramming with a single gene, Oct4 (6). We duration of the small molecules and were able ‡Corresponding author. E-mail: [email protected](H.D.); [email protected] (Y.Z.) next treated OG-MEFs with VC6T plus the chem- to generate 1 to 20 CiPSC colonies from 50,000 SCIENCE VOL 341 9 AUGUST 2013


January - March 2013 If you met Leanne Hosking, you'd probably never guess she is battling a neuromuscular disease. She is a lively 32 year old woman who works full-time, exercises daily, and enjoys traveling the world. But this wasn't the case three 1000 John R, Suite 111 years ago when she suffered from severe muscle weakness and fatigue. She couldn't drive at night and found it difficult to complete everyday tasks like getting


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