The DOI matches its Crossref record; 56 of the 65 references it cites were found in a registry or an index.
Social Influence Bias: A Randomized Experiment
Abstract
Follow the Leader?: The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647 ) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web site on which readers could vote and comment on posted comments. Data were collected and analyzed after the Web site administrators arbitrarily voted positively or negatively (or not at all) as the first comment on more than 100,000 posts. False positive entries led to inflated subsequent scores, whereas false negative initial votes had small long-term effects. Both the topic being commented upon and the relationship between the poster and commenter were important. Future efforts will be needed to sort out how to correct for such effects in polls or other collective intelligence systems in order to counter social biases.
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“prior ratings and reviews introduce significant bias in individual rating behavior and writing style, creating a herding effect”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Muchnik et al., 2013
In the citing paper
“The gold standard for such causal analyses are randomized controlled experiments (Aral and Walker, 2011; Muchnik et al., 2013).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Muchnik et al., 2013
In the citing paper
“Many large-scale studies have reported evidence of online ecosystems altering decision-making of crowds [3] and influencing real-world voting of millions of people [4].”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [3]
In the citing paper
“This finding is in full agreement with previous works showing how online ecosystems acted upon society by altering the emotions [19] and beliefs [3, 4] of large populations of individuals.”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [3]
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
Social Theory and Social Structure
Ross, E. J., & Merton, R. Κ. (1958) · The American Catholic Sociological Review · doi:10.2307/3709394 · Cited by 10,318
The Kolmogorov-Smirnov Test for Goodness of Fit
Massey, F. J. (1951) · Journal of the American Statistical Association · doi:10.1080/01621459.1951.10500769 · Cited by 7,210
A Simple Model of Herd Behavior
Banerjee, A. V. (1992) · The Quarterly Journal of Economics · doi:10.2307/2118364 · Cited by 6,556
Identification of Endogenous Social Effects: The Reflection Problem
Manski, C. F. (1993) · The Review of Economic Studies · doi:10.2307/2298123 · Cited by 6,209
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
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9 of the 65 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Anderson L. R., Holt C. A.Information cascades in the laboratory, Am. Econ. Rev. 87, 847 (1997).
- B. Gu M. Lin “The dynamics of online consumer reviews ” Workshop on Information Systems and Economics (WISE) (2006).
- S. E. Asch in Groups Leadership and Men H. Guetzkow Ed. (Carnegie Press Pittsburgh PA 1951) pp. 177–190.
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Sorted by citations
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Lee, H. A., Choi, A. A., Sun, T., & Oh, W. (2021) · Information Systems Research · doi:10.1287/isre.2021.1029 · Cited by 32
The Extreme Distribution of Online Reviews: Prevalence, Drivers and ImplicationsPreprint
Schoenmueller, V., Netzer, O., & Stahl, F. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3100217 · Cited by 32
Srinivasan, K. B., Danescu-Niculescu-Mizil, C., Lee, L., & Tan, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09563 · Cited by 31
“Prior research has studied the effect of exposure to (violating) such norms on community-level or discussion-level behavior [25–27].”
BackgroundWas this review helpful to you?
Sipoš, R., Ghosh, A., & Joachims, T. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2567998 · Cited by 32
“It is also conceivable that the history of votes so far also changes the user’s perception of a review itself, as in herding phenomena [12].”
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Fang, J., Hu, L., Hossin, M. A., Yang, J., & Shao, Y. (2019) · International Journal of Electronic Commerce · doi:10.1080/10864415.2019.1655206 · Cited by 32
Design of Randomized Experiments in Networks
Walker, D., & Muchnik, L. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2363674 · Cited by 30
“[61] who show that minor manipulation of the perceived scores of user-generated comments guided consequent user votes and resulted in herding, significantly affecting the content’s final score.”
Self-citationGraham, T., & Rodriguez, A. (2021) · Social Media + Society · doi:10.1177/20563051211047667 · Cited by 47
“Muchnik et al.’s (2013) study on a “social news aggregation website” showed that the visibility of prior upvotes and downvotes “created significant bias in individual rating behaviour” (p. 647).”
BackgroundReason against the machine? Future directions for mass online deliberation
Shortall, R., Itten, A. V., van der Meer, M., Murukannaiah, P. K., & Jonker, C. M. (2022) · Frontiers in Political Science · doi:10.3389/fpos.2022.946589 · Cited by 33
“Humans tend to be highly influenced by the opinions of others: for instance, studies show that comments with an early up-vote are 25% more highly rated in the end, regardless of their quality (Muchnik et al., 2013; Maia and Rezende, 2016).”
BackgroundWhy are ratings so high in the sharing economy? Evidence based on guest perspectives
Bulchand‐Gidumal, J., & González, S. M. (2019) · Current Issues in Tourism · doi:10.1080/13683500.2019.1602597 · Cited by 32
“Muchnik, Aral, and Taylor (2013) named this phenomenon social influence bias and showed that previous positive ratings increased the probability of posting favourable reviews.”
BackgroundRosenberg, L. B., & Willcox, G. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29516-5_79 · Cited by 36
Ideology and Composition Among an Online Crowd: Evidence from Wikipedians
Greenstein, S. M., Gu, G., & Zhu, F. (2020) · Management Science · doi:10.1287/mnsc.2020.3661 · Cited by 32
“Several studies have proposed new approaches to aggregate opinions from the crowds to minimize bias (e.g., Fan et al. 2005, Muchnik et al. 2013, Prelec et al. 2017, Wang et al. 2019).”
BackgroundReviews and Self-Selection Bias with Operational Implications
Chen, N., Li, A., & Talluri, K. T. (2019) · Management Science · doi:10.1287/mnsc.2020.3892 · Cited by 30
“Muchnik et al. (2013), using randomized experiments, investigate another source of bias that they call social bias , where previous reviews influence a customer’s ratings.”
Resilient help to switch and overlap hierarchical subsystems in a small human group
Fujii, K., Yokoyama, K., KOYAMA, T., Rikukawa, A., Yamada, H., & Yamamoto, Y. (2016) · Scientific Reports · doi:10.1038/srep23911 · Cited by 33
“The important feature is how agents should cooperatively interact, because the social interaction may have sometimes negative effects 34,35 on the whole sys-tem.”
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Belleflamme, P., & Peitz, M. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3128141 · Cited by 32
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de Marneffe, M.-C., & Potts, C. (2017) · Springer eBooks · doi:10.1007/978-94-024-0881-2_16 · Cited by 30
“This can give them a veracity that is often lacking in controlled annotation projects and crowdsourced annotation projects, and it means that they can be studied scientifically in their own right (e.g., Muchnik et al. 2013).”
BackgroundMotivating User-Generated Content: The Unintended Consequences of Incentive Thresholds
He, L., Luo, J., Tang, Y., Wu, Z., & Zhang, H. (2023) · MIS Quarterly · doi:10.25300/misq/2022/17369 · Cited by 37
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Freisinger, E., Unfried, M., & Schneider, S. (2023) · Journal of Product Innovation Management · doi:10.1111/jpim.12708 · Cited by 32
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Saifee, D. H., Bardhan, I. R., Lahiri, A., et al. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2019.1661093 · Cited by 30
The effects of feedback on human behavior in social media: an inverse reinforcement learning model
Das, S., & Lavoie, A. (2014) · doi:10.5555/2615731.2615837 · Cited by 30
Discussion Quality Diffuses in the Digital Public Square
Berry, G., & Taylor, S. J. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052666 · Cited by 28
Self-citationRandomized Experiments to Detect and Estimate Social Influence in Networks
Taylor, S. J., & Eckles, D. (2018) · Computational social sciences · doi:10.1007/978-3-319-77332-2_16 · Cited by 28
“Bond et al (2012) and Taylor et al (2013) conducted experiments by implementing them in partnership with Facebook (see Bond et al., this volume), while Muchnik et al (2013) partnered with a social news website to introduce an experimental change.”
MethodSelf-citationHow Endogenous Crowd Formation Undermines the Wisdom of the Crowd in Online Ratings
Mens, G. L., Kovács, B., Avrahami, J., & Kareev, Y. (2018) · Psychological Science · doi:10.1177/0956797618775080 · Cited by 32
“This happens even when the valence of earlier ratings does not affect the valence of subsequent ratings (i.e., there is no social influence bias, as in Muchnik et al., 2013).”
BackgroundZhang, X., Ferreira, P., de Matos, M. G., & Belo, R. (2021) · MIS Quarterly · doi:10.25300/misq/2021/14971 · Cited by 29
Avi Goldfarb, B., & Tucker, C. E. (2019) · Handbook of economics marketing · doi:10.1016/bs.hem.2019.04.004 · Cited by 48
The Pitfalls of Review Solicitation: Evidence from a Natural Experiment on TripAdvisor
Gao, B., Wang, J., Xiaojie, D., & Guo, Y. (2024) · Management Science · doi:10.1287/mnsc.2023.01006 · Cited by 34
Knowledge generation and sharing in online communities: current trends and future directions
Tausczik, Y., & Huang, X. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.009 · Cited by 30
Herding and Software Adoption: A Re-Examination Based on Post-Adoption Software Discontinuance
Zhao, X., Tian, J., & Xue, L. (2018) · Journal of Management Information Systems · doi:10.1080/07421222.2020.1759941 · Cited by 29
“[35] find that positive rating can elicit more positive ratings from users than a negative rating, resulting in a biased accumulated rating toward the positive side.”
BackgroundInfluentialAdomavičius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2022) · MIS Quarterly · doi:10.25300/misq/2022/16301 · Cited by 28
Dong, H., Ren, J., Padmanabhan, B., & Nickerson, J. V. (2019) · Information & Management · doi:10.1016/j.im.2021.103588 · Cited by 30
Words matter: a systematic review of communication in non-native aquatic species literature
Golebie, E., van Riper, C. J., Arlinghaus, R., et al. (2022) · NeoBiota · doi:10.3897/neobiota.74.79942 · Cited by 29
Consumers, experts, and online product evaluations: Evidence from the brewing industry
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“Muchnik et al. (2013) implement a field experiment with a social news aggregation web site and find that a randomly assigned positive initial rating for an item leads to large and lasting effects in the amount of positive ratings received by the item.”
BackgroundMa, L., Gong, M., Yan, J., Yuan, F., & Du, H. (2016) · Information Sciences · doi:10.1016/j.ins.2016.10.035 · Cited by 27
Idea evaluation in innovation contest platforms: A network perspective
Özaygen, A., & Balagué, C. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.06.001 · Cited by 32
Write like i write: Herding in the language of online reviews
Michael, L., & Otterbacher, J. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14511 · Cited by 28
“…some researchers argue that it is “nearly impossible” to distinguish genuine herding behaviors from uninfluenced behaviors using observational data (Muchnik, Aral, and Taylor 2013), others have demonstrated that under certain conditions, it might be possible to infer causal relationships in such…”
BackgroundMethodEmpirically Understanding the Potential Impacts and Process of Social Influence in Human-AI Teams
Flathmann, C., Duan, W., McNeese, N. J., Hauptman, A. I., & Zhang, R. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637326 · Cited by 39
Managing for the future: managerial short-termism impact on corporate ESG performance in China
Deng, G., Liu, H., Yan, J., & Ma, S. (2024) · European Journal of Finance · doi:10.1080/1351847x.2024.2387622 · Cited by 40
“Muchnik, Aral, and Taylor (2013) have researched that social sentiment and public opinion play a significant role in shaping individual choices.”
The Lifecycles of Apps in a Social Ecosystem
Kloumann, I. M., Adamic, L. A., Kleinberg, J. M., et al. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741684 · Cited by 28
“the word-ofmouth effect, is playing a increasingly important role at driving the adoption of products and services [3, 12, 14].”
BackgroundVoting Behaviour and Power in Online Democracy: A Study of LiquidFeedback in Germany's Pirate Party
Kling, C. C., Kunegis, J., Hartmann, H., et al. (2015) · ePrints Soton (University of Southampton) · doi:10.48550/arxiv.1503.07723 · Cited by 28
Cognitive Biases in Chronic Illness and Their Impact on Patients' Commitment
Savioni, L., & Triberti, S. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.579455 · Cited by 31
“Others’ influence on decisions can often lead to a wrong evaluation of the choices with a tendency to take a greater risk (Gardner and Steinberg, 2005; Muchnik et al., 2013).”
BackgroundOptimizing the Recency-Relevancy Trade-off in Online News Recommendations
Chakraborty, A., Ghosh, S., Ganguly, N., & Gummadi, K. P. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052656 · Cited by 28
“Similarly, efforts have been made to predict popularity classes [17, 19], understand different popularity dynamics [34, 35], and whether they lead to the emergence of self-fulfilling prophecies [36], or social influence biases [37].”
BackgroundOnline group influence and digital product consumption
Mu, J., Thomas, E. F., Qi, J., & Tan, Y. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0578-5 · Cited by 27
Internet Public Opinion Risk Grading under Emergency Event Based on AHPSort II-DEMATEL
Mei, Y., Tu, Y., Xie, K., et al. (2019) · Sustainability · doi:10.3390/su11164440 · Cited by 28
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BackgroundAggregation of Consumer Ratings: An Application to Yelp.comPreprint
Dai, W. (., Jin, G. Z., Lee, J., & Luca, M. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2518998 · Cited by 24
Random Voting Effects in Social-Digital Spaces
Weninger, T., Johnston, T. J., & Glenski, M. (2015) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2700171.2791054 · Cited by 27
Dumas, M., Barker, J. L., & Power, E. (2021) · Philosophical Transactions of the Royal Society of London. Biological Sciences · doi:10.1098/rstb.2020.0298 · Cited by 18
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Coviello, L., Fowler, J. H., & Franceschetti, M. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2366052 · Cited by 27
“KEYWORDS | Influence; instrumental variables; nonexperimental methods; semantic expression; social networks”
MethodThe Effectiveness of Marketing Strategies in Social Media
Adamopoulos, P., & Todri, V. (2015) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788597 · Cited by 26
“On the other hand, a large fan base might positively affect customers’ decision to follow a brand through a social influence bias [24] contributing to a “richget-richer” effect.”
BackgroundFactors Affecting Individual Decisions Based on Business Legitimacy
Diéz‐Martín, F., Blanco‐González, A., & Román, C. P. (2018) · Journal of Promotion Management · doi:10.1080/10496491.2018.1443308 · Cited by 26
Xue, F., Dong, L., Gao, B., Yu, Z., & Taras, V. (2020) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-02-2020-0134 · Cited by 27
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“Similarly, limitations on data collection and a di ff erent source of skew-ness or bias exist such as selection bias as well as assortative bias in dynamic social networks (Muchnik, Aral, and Taylor 2013).”
BackgroundAggregation of Consumer Ratings: An Application to Yelp.com
Dai, W., Jin, G. Z., Lee, J., & Luca, M. (2012) · National Bureau of Economic Research · doi:10.3386/w18567 · Cited by 26
Optimizing the recency-relevance-diversity trade-offs in non-personalized news recommendations
Chakraborty, A., Ghosh, S., Ganguly, N., & Gummadi, K. P. (2019) · Information Retrieval Journal · doi:10.1007/s10791-019-09351-2 · Cited by 24
“(1974) on the ‘public sphere’, Novendstern (2011) argued that news is a part of people’s ‘public discourse’, using which they can participate in community discussions.”
BackgroundUnderstanding Social Causalities Behind Human Action Sequences
Cai, R., Zhang, Z., Hao, Z., & Winslett, M. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2556724 · Cited by 24
No oops, you won't do it again: mechanisms for self-correction in crowdsourcing
Shah, N. B., & Zhou, D. (2016) · International Conference on Machine Learning · Cited by 22
Predicting User-Interactions on Reddit
Glenski, M., & Weninger, T. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3120993 · Cited by 24
Zhang, Z., Zhang, Z., & Chen, P.-Y. (2021) · MIS Quarterly · doi:10.25300/misq/2021/14312 · Cited by 29
Li, H., Meng, F., & Hudson, S. (2021) · Journal of Hospitality & Tourism Research · doi:10.1177/10963480211030313 · Cited by 25
“Ho & Dempsey, 2010; Phelps et al., 2004) and online context (Sundaram et al., 1998). Moreover, Sundaram et al. (1998) argued that the motivation of altruism exists in both positive- and negative-WOM, although all other motivations are different.”
BackgroundInfluentialMa, D., Li, S., Du, J. T., Bu, Z., Cao, J., & Sun, J. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107042 · Cited by 25
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“He observes that the people were not independent – ‘you need people to be thinking for themselves, rather than following the lead of those around them’ – echoing the findings of Massanari (2015a) and others (Bucher, 2012: 1176; Muchnik et al., 2013).”
BackgroundZhu, K., Walker, D., & Muchnik, L. (2020) · Information Systems Research · doi:10.1287/isre.2019.0899 · Cited by 25
Self-citationHuman swarming, a real-time method for parallel distributed intelligence
Rosenberg, L. B. (2015) · Swarm/Human Blended Intelligence Workshop (SHBI) · doi:10.1109/shbi.2015.7321685 · Cited by 24
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“Experiments [19, 30] revealed that small positive manipulations would encourage more positive future ratings, creating accumulative herding that boosts the final average ratings.”
BackgroundInfluentialBulut, E., & Duru, O. (2017) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-62338-2_3 · Cited by 30
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Galit Shmueli, A. (2016) · Quality Engineering · doi:10.1080/08982112.2016.1210979 · Cited by 27
“…available BBD, e.g., whether conspicuous consumption represents an investment in social capital (Hinz et al., 2015), the effect of social influence (Muchnik et al., 2014), and the impact of teachers on student outcomes (Chetty et al., 2014) — while others identify and ask new questions, often…”
BackgroundMethodInfluentialChellappoo, A. (2020) · Synthese · doi:10.1007/s11229-020-02565-8
Krafft, P. M., Macy, M. W., & Pentland, A. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998354 · Cited by 24
User-Generated Content and Social MediaPreprint
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Edenberg, E. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780192893338.003.0015 · Cited by 24
“…a particular story shared on social media, they tend to go along with the crowd signaling their endorsement of the content, suppress their own reservations, and refrain from sharing information that contradicts the dominant position (Sunstein 2017, 100-101; Kuran 1997; Muchnik et al. 2013).”
BackgroundBiases in scholarly recommender systems: impact, prevalence, and mitigation
Färber, M., Coutinho, M., & Yuan, S. (2023) · Scientometrics · doi:10.1007/s11192-023-04636-2 · Cited by 31
“Furthermore, being aware of what is popular can be viewed as a type of social influence; an individual’s behavior may be influenced by colleagues or neighbors’ choices (Muchnik et al., 2013).”
BackgroundYan, B., Jian, L., Ren, R., Fulk, J. L., Sidnam-Mauch, E., & Monge, P. R. (2020) · Communication Research · doi:10.1177/0093650220915033 · Cited by 23
“As a result, crowd members provide less diverse predictions compared to when working independently, leading to herding and biases in their judgments (Hong et al., 2020; Lorenz et al., 2011; Muchnik et al., 2013; Salganik et al., 2006; Salganik & Watts, 2009; Toyokawa et al., 2018).”
BackgroundInfluentialFooled by facts: quantifying anchoring bias through a large-scale experiment
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“Considering the design of social media platforms which “bombard” the users with information of other users’ actions and decisions (social information) and previously reported influence of social information on decision making [17], it becomes ever more relevant to ask how cognitive biases in general”
BackgroundWhen the design of climate policy meets public acceptance: An adaptive multiplex network model
Lipari, F., Lázaro, L., Escribano, G., Sánchez, Á., & Antonioni, A. (2023) · Ecological Economics · doi:10.1016/j.ecolecon.2023.108084 · Cited by 29
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Abeliuk, A., Berbeglia, G., Cebrián, M., & Hentenryck, P. V. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121934 · Cited by 23
“In particular, Salganik et al suggested that social influence contributes to both unpredictability and inequality of success, with follow-up experiments confirming these initial findings [9–12] (see also [13] for a perspective on the MUSICLAB paper).”
ResultAdded Value from Secondary Use of Person Generated Health Data in Consumer Health Informatics
Hsueh, P.-Y. S., Cheung, Y. K., Dey, S., Kim, K. K., Martin-Sanchez, F. J., Petersen, S. K., & Wetter, T. C. (2017) · Yearbook of Medical Informatics · doi:10.15265/iy-2017-009 · Cited by 24
“It has been observed inside [101, section 3.3.1.1] and outside [103] the internet that clever rhetoric in the absence of true knowledge, maybe better characterized as bullying, can influence the selection and decision-making process in communities.”
BackgroundInfluentialThe dynamics of collective social behavior in a crowd controlled game
Aleta, A., & Moreno, Y. (2019) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0200-1 · Cited by 24
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Simoiu, C., Sumanth, C., Mysore, A., & Goel, S. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5271 · Cited by 25
“But, on the other hand, there is also worry that such social influence could result in herding, which in turn could decrease collective performance (Lorenz et al. 2011; Muchnik, Aral, and Taylor 2013; Salganik, Dodds, and Watts 2006).”
BackgroundTopîrceanu, A., Udrescu, M., Vlăduţiu, M., & Mărculescu, R. (2016) · PeerJ Computer Science · doi:10.7717/peerj-cs.42 · Cited by 22
Enhancing participation to health screening campaigns by group interactions
Burioni, R., Contucci, P., Fedele, M., Vernia, C., & Vezzani, A. (2015) · Scientific Reports · doi:10.1038/srep09904 · Cited by 22
Third-Party Reviews and Quality Provision
Kim, K., Chung, K., & Lim, N. (2019) · Management Science · doi:10.1287/mnsc.2018.3082 · Cited by 22
A social science‐inspired complexity policy: Beyond the mantra of incentivization
Squazzoni, F. (2014) · Complexity · doi:10.1002/cplx.21520 · Cited by 22
Ideological Segregation among Online Collaborators: Evidence from Wikipedians
Greenstein, S. M., Gu, Y. X., & Zhu, F. (2016) · National Bureau of Economic Research · doi:10.3386/w22744 · Cited by 21
The influence of community structure on opinion expression: an agent-based model
Cabrera, B., Roß, B., Röchert, D., et al. (2021) · Journal of Business Economics · doi:10.1007/s11573-021-01064-7 · Cited by 21
“In general, customers adapt their reviews or ratings of products to the general opinion of the crowd (Muchnik et al. 2013; Jiang and Wu 2017).”
BackgroundMethods, Theories, Data, and the Social Dynamics of Organizational Research
Bitektine, A. B., & Miller, D. (2014) · Journal of Management Inquiry · doi:10.1177/1056492614546897 · Cited by 24
Social influence protects collective decision making from equality bias.
Hertz, U., Romand-Monnier, M., Kyriakopoulou, K., & Bahrami, B. (2015) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/xhp0000145 · Cited by 23
Mitigating Overexposure in Viral Marketing
Abebe, R., Adamic, L. A., & Kleinberg, J. M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11282 · Cited by 23
“negative ratings tend to be compensated for in later reviews, positive reviews can lead to herding effects [19].”
BackgroundDeviation-based spam-filtering method via stochastic approach
Lee, D., Lee, M. J., & Kim, B. J. (2018) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/121/68004 · Cited by 21
Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform
Askay, D. A., Metcalf, L. E., Rosenberg, L. B., & Willcox, G. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.061 · Cited by 21
“As these decisions are made in serial, earlier decisions by some can influence future decisions of others through social influence and information cascades [8].”
BackgroundCheng, J., Chen, B., & Huang, Z. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103257 · Cited by 24
Information cascades in online reading: an empirical investigation of panel data
Liu, Q., & Zhang, L. (2014) · Library Hi Tech · doi:10.1108/lht-06-2014-0052 · Cited by 20
“Accordingly, many researchers use network externalities and word-of-mouth effects to explain online herding behavior (Kauffman et al., 2000; Lee et al., 2009; Pontiggia and Virili, 2010; Muchnik et al., 2013).”
BackgroundResultInfluentialThe evolving cobweb of relations among partially rational investors
Lellis, P. D., Meglio, A. D., Garofalo, F., & Iudice, F. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171891 · Cited by 20
Negative Peer Feedback and User Content Generation: Evidence From a Restaurant Review Platform
Zhu, K., Khern-am-nuai, W., & Yu, Y. (2024) · Production and Operations Management · doi:10.1177/10591478231224941 · Cited by 20
Heterogeneous peer effects of college roommates on academic performance
Cao, Y., Zhou, T., & Gao, J. G. (2024) · Nature Communications · doi:10.1038/s41467-024-49228-7 · Cited by 27
Sentiment expression conditioned by affective transitions and social forces
Sudhof, M., Emilsson, A. G., Maas, A. L., & Potts, C. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623687 · Cited by 19
“This can be thought of as part of a growing literature that seeks to understand online, user-provided metadata as both reflecting and shaping complex social processes concerning influence, group cohesion, individual assertion of identity, and sentiment diffusion in networks [1, 5, 8, 20, 21, 38].”
BackgroundStatistical Analysis of Player Behavior in Minecraft
Müller, S., Kapadia, M., Frey, S., Klinger, S., Mann, R., Solenthaler, B., Sumner, R., & Groß, M. H. (2015) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 20
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Pei, S., Tang, S., & Zheng, Z. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124848 · Cited by 20
“Additionally, the spreading dynamics in social networks are also greatly affected by other human-related factors, such as homophily [51, 52], activity [53, 54], social reinforcement [55], and social influence bias [56].”
BackgroundTemporal scaling in information propagation
Huang, J., Li, C., Wang, W., et al. (2014) · Scientific Reports · doi:10.1038/srep05334 · Cited by 20
THE DYNAMIC EFFECTS OF ONLINE PRODUCT REVIEWS ON PURCHASE DECISIONS
Chen, J., Kou, G., & Peng, Y. (2018) · Technological and Economic Development of Economy · doi:10.3846/tede.2018.4545 · Cited by 23
Swarm intelligence? Stock opinions of the crowd and stock returns
Breitmayer, B., Massari, F., & Pelster, M. (2019) · International Review of Economics and Finance · doi:10.1016/j.iref.2019.08.006 · Cited by 19
“This observation is consistent with previous observations documented in the literature: Muchnik et al. (2013) and Tang et al. (2017) document a positivity bias against negative sentiment.”
BackgroundResultInfluential
Metadata
- Authors
- Muchnik, L., Aral, S., & Taylor, S. J.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Massachusetts Institute of Technology (US)
- New York University (US)
- Corresponding author
- Aral, Sinan
- Venue
- Science
- Published
- 2013-08-09
- Volume
- 341
- Issue
- 6146
- Pages
- 647-651
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2063248556
- Cited by
- 892
- References
- 65
- Citations by year
- 2026: 100 · 2025: 60 · 2024: 52 · 2023: 39 · 2022: 60 · 2021: 61 · 2020: 69 · 2019: 72 · 2018: 78 · 2017: 96 · 2016: 74 · 2015: 65 · 2014: 53 · 2013: 12
- FWCI
- 39.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- herding behavior, online ratings, collective intelligence, collective judgment, decision-making
- MeSH terms
- Decision Making, Humans, Intelligence, Politics, Public Opinion, Social Environment, Bias, Focus Groups, Internet, Behavior Control, Friends
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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