Displaying things in common to encourage friendship formation: A large randomized field experiment


Friendship formation is of central importance to online social network sites and to society, but can suffer from significant and unequal frictions. In this study, we demonstrate that social networks and policy makers may use an IT-facilitated intervention – displaying things in common (TIC) between users (mutual hometown, interest, education, work, city) – to encourage friendship formation, especially among people who are different from each other. Displaying TIC may update an individual’s belief about the shared similarity with another and reduce information friction that may be hard to overcome in offline communication. In collaboration with an online social network, we design and implement a randomized field experiment, which randomly varies the prominence of different types of things in common information when a user (viewer) is browsing a non-friend’s profile. The dyad-level exogenous variation, orthogonal to any (un)observed structural factors in viewer-profile’s network, allows us to cleanly isolate the role of individuals’ preference for TIC in driving network formation and homophily. We find that displaying TIC to viewers may significantly increase their probability of sending a friend request and forming a friendship, and is especially effective for pairs of people who have little in common. Such findings suggest that information intervention is a very effective and zero-cost approach to encourage the formation of weak ties, and also provide the first experimental evidence on the crucial role of individuals’ preference (versus structural embeddedness) in network formation. We further demonstrate that displaying TIC could improve friendship formation for a wide range of viewers with different demographics and friendship status, and is more effective when the TIC information is more surprising to the viewer. Our study offers actionable insights to social networks and policy makers on the design of information intervention to encourage friendship formation and improve the diversity of the friendship, at both an aggregate and an individual level.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. 1.

    Several major SNs have made increasing the number of friends a key goal in their operations (Price 2012): e.g. Linkedin aims to get a user to reach X friends in Y days, and Facebook and Twitter use similar goal metrics.

  2. 2.

    The viewer may either have little information about the profile’s interest, or have an biased perception (Goel et al. 2010). In both scenarios, the displayed TIC information would help update the viewer’s belief.

  3. 3.

    Despite lively discussions on the importance of friendship diversity (Kossinets and Watts 2009; Eagle et al. 2010; Granovetter 1977) in facilitating interactions among different groups (Bakshy et al. 2015), very little is understood about how online social networks can actively help ‘build’ the weak ties among people with different background.

  4. 4.

    Finally, our study also extends a large stream of literature in Marketing and IS on the reduction of information friction in online platforms and marketplaces. Previous literature has focused on how IT artifacts (online reviews, product recommendations) can be used to reduce friction in user-product interactions Fradkin (2017), Forman et al. (2008), Oestreicher-Singer and Sundararajan (2012), and Fleder and Hosanagar (2009). Our study suggests a new route in which IT could reduce the friction in user-user interaction, and open up a new area of research on the role of IT in moderating the structure, evolution and value of user-user network (Oestreicher-Singer et al. 2013; Hosanagar et al. 2013).

  5. 5.

    For instance, those viewer-profile pairs with 2 TIC may have a higher friendship formation rate than those with only 1 TIC, not because of the additional TIC at display, but because pairs with 2 TIC are likely to have more mutual friends and more interaction opportunities (i.e. structural factors) than pairs with 1 TIC. In Online Appendix C, we confirm the above insight and empirically demonstrate that the correlation between number of things in common in a pair and the corresponding friendship formation rate is not only biased, but even opposite to the true causal effect in sign (Fig. 24)

  6. 6.

    Observational data often lacks of detailed information on the structural factor such as interaction history and friendship structure (McPherson et al. 2001), which is needed to control for meeting bias and triadic closure. Even more fundamentally, the endogenous correlation between things in common, meeting bias, and network structure makes it almost impossible to isolate the role of preference in friendship formation from observational studies (Currarini et al. 2010).

  7. 7.

    We use ‘articulated friendship’ to denote that the friendship on SN sites is a unique type of social network connection, which is valuable by itself (in the creation and spread of information) and may differ from the offline friendship. For instance, the interaction frequency and tie strength of articulated friendship on average may be lower (weaker) as compared to the offline friendship.

  8. 8.

    Specifically, the viewer and profile can be represented as two high-dimensional n × 1 vectors: each row representing the value of a profile field entry or a page like decision. Their TIC are calculated from the intersect of the two vectors.

  9. 9.

    We also check the balance of a series of user covariates (e.g. viewer/profile gender, friend count) across the control and treatment groups and do not find any significant differences. All tables are available upon request.

  10. 10.

    Treatment effect from variance estimators yields the same point estimate and more statistically significant results.

  11. 11.

    As discussed in the section above, we focus on discussing the treatment effect for those pairs with 1 or 2 TIC, and present the results for pairs with 3 TIC in Figs. 13 and 14

  12. 12.

    Same pattern holds for pairs with 3 actual TIC (Figs. 13 and 14) but sample size is too small as shown in Fig. 9.

  13. 13.

    We are unable to find any evidence that the treatment induces any significant positive or negative effects on the rate that requests are accepted (see Fig. 15 for detailed analysis).

  14. 14.

    For results in Figs. 1617 and 18, we also have tables with detailed regression results (available upon request)

  15. 15.

    Without loss of generalizability, we focus on the effect of one type of TIC (‘a’) in the discussion.

  16. 16.

    Social influence spread on existing ties is strongest when the tie shares mutual friends and multiple TICs.

  17. 17.

    Though preference over similar others is a underlying driver of homophily among weak ties, the preference, by itself, does not reinforce tie formation among people who already share mutual friends thus cannot lead to a significant level of structural homophily. Preference may connect people with different background, but the rest of network evolution might be driven by structural factor such as meeting opportunities and triadic closure

  18. 18.

    Another potential mechanism is attention disruption: showing TIC on a profile card would disrupt the monotonicity of seeing many profile cards in a row. Such additional attention may lead to an increase likelihood of friendship formation. The attention disruption explanation suggests that the effect of TIC is stronger in the early stage of browsing, especially during the transition between control and treatment. However, we perform an exploratory analysis on whether the effect of displaying TIC would vary across profiles with different positions in the browsing sequence but did not find any significant pattern. The evidence might indicate that attention disruption is not playing a major role underlying the process. We thank one reviewer for the suggestion.

  19. 19.

    The positive correlation is tapering off at the right end (in the area where surprisal> 9 shannons). Since the number of observations is much smaller in this area (as revealed from the wider confidence interval), it does not strongly affect the overall trend. The coefficient of surprisal is positive if we fit a linear relationship on the data.

  20. 20.

    Interestingly, at an aggregate ‘type’ level, the information across different types of TICs are likely to be substitutes for one another. We can identify such relationship by examining the effect of TIC display when the viewer-profile pairs share two TIC (Figs. 11 and 21). As shown from the 9 panels in Fig. 21, in most scenarios, the effect of displaying two types of things in common is not additive, demonstrating no clear positive complementarity between them is identified. Thus, SN sites could use the information-theoretic framework to guide the optimal display of TIC.

  21. 21.

    One exception is Phan and Airoldi (2015), in which the authors carefully design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster.


  1. Altenburger, K.M., & Ugander, J. (2018). Monophily in social networks introduces similarity among friends-of-friends. Nature Human Behaviour, 2(4), 284.

    Google Scholar 

  2. Ameri, M., Honka, E., & Xie, Y. (2017). A structural model of network dynamics: Tie formation, product adoption, and content generation. Working Paper.

  3. Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51), 21544–21549.

    Google Scholar 

  4. Aral, S., & Walker, D. (2011). Creating social contagion through viral product design: a randomized trial of peer influence in networks. Management Science, 57(9), 1623–1639.

    Google Scholar 

  5. Aral, S., & Walker, D. (2012). Identifying influential and susceptible members of social networks. Science, 1215842.

  6. Aral, S., & Walker, D. (2014). Tie strength, embeddedness, and social influence: a large-scale networked experiment. Management Science, 60(6), 1352–1370.

    Google Scholar 

  7. Bakshy, E., Eckles, D., Yan, R., & Rosenn, I. (2012a). Social influence in social advertising: evidence from field experiments. In Proceedings of the 13th ACM Conference on Electronic Commerce (pp. 146–161): ACM.

  8. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012b). The role of social networks in information diffusion. In Proceedings of the 21st international conference on World Wide Web (pp. 519–528): ACM.

  9. Bakshy, E., & Eckles, D. (2013). Uncertainty in online experiments with dependent data: an evaluation of bootstrap methods. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1303–1311): ACM.

  10. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239), 1130–1132.

    Google Scholar 

  11. Bala, V., & Goyal, S. (2000). A noncooperative model of network formation. Econometrica, 68(5), 1181–1229.

    Google Scholar 

  12. Bapna, R., & Umyarov, A. (2015). Do your online friends make you pay? a randomized field experiment on peer influence in online social networks. Management Science, 61(8), 1902–1920.

    Google Scholar 

  13. Bapna, R., Ramaprasad, J., Shmueli, G., & Umyarov, A. (2016). One-way mirrors in online dating: a randomized field experiment. Management Science, 62 (11), 3100–3122.

    Google Scholar 

  14. Bapna, R., Liangfei, Q., & Rice, S. (2017). Repeated interactions versus social ties: Quantifying the economic value of trust, forgiveness, and reputation using a field experiment. MIS Quarterly, 41(3).

  15. Bénabou, R., & Tirole, J. (2016). Mindful economics: The production, consumption, and value of beliefs. Journal of Economic Perspectives, 30(3), 141–64.

    Google Scholar 

  16. Berscheid, E., & Reis, H.T. (1998). Attraction and close relationships. The Handbook of Social Psychology.

  17. Brzozowski, M.J., & Romero, D.M. (2011). Who should i follow? recommending people in directed social networks. In ICWSM.

  18. Burke, M., & Kraut, R.E. (2016). The relationship between facebook use and well-being depends on communication type and tie strength. Journal of Computer-Mediated Communication, 21(4), 265–281.

    Google Scholar 

  19. Bursztyn, L., Egorov, G., & Fiorin, S. (2017). From extreme to mainstream: How social norms unravel. Technical report, National Bureau of Economic Research.

  20. Centola, D. (2015). The social origins of networks and diffusion. American Journal of Sociology, 120(5), 1295–1338.

    Google Scholar 

  21. Church, K.W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.

    Google Scholar 

  22. Currarini, S., Jackson, M.O., & Pin, P. (2009). An economic model of friendship: homophily, minorities, and segregation. Econometrica, 77(4), 1003–1045.

    Google Scholar 

  23. Currarini, S., Jackson, M.O., & Pin, P. (2010). Identifying the roles of race-based choice and chance in high school friendship network formation. Proceedings of the National Academy of Sciences, 107(11), 4857–4861.

    Google Scholar 

  24. Dhar, V., Geva, T., Oestreicher-Singer, G., & Sundararajan, A. (2014). Prediction in economic networks. Information Systems Research, 25(2), 264–284.

    Google Scholar 

  25. Eagle, N., Macy, M., & Claxton, R. (2010). Network diversity and economic development. Science, 328(5981), 1029–1031.

    Google Scholar 

  26. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.

  27. Eckles, D., Kizilcec, R.F., & Bakshy, E. (2016). Estimating peer effects in networks with peer encouragement designs. Proceedings of the National Academy of Sciences, 113(27), 7316–7322.

    Google Scholar 

  28. Ely, J., Frankel, A., & Kamenica, E. (2015). Suspense and surprise. Journal of Political Economy, 123(1), 215–260.

    Google Scholar 

  29. Felmlee, D., Sprecher, S., & Bassin, E. (1990). The dissolution of intimate relationships: a hazard model. Social Psychology Quarterly, 13–30.

  30. Fischer, M.J. (2008). Does campus diversity promote friendship diversity? a look at interracial friendships in college. Social Science Quarterly, 89(3), 631–655.

    Google Scholar 

  31. Fisman, R., Iyengar, S.S., Kamenica, E., & Simonson, I. (2006). Gender differences in mate selection: Evidence from a speed dating experiment. The Quarterly Journal of Economics, 121(2), 673–697.

    Google Scholar 

  32. Fisman, R., Iyengar, S.S., Kamenica, E., & Simonson, I. (2008). Racial preferences in dating. The Review of Economic Studies, 75(1), 117–132.

    Google Scholar 

  33. Fleder, D., & Hosanagar, K. (2009). Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Science, 55(5), 697–712.

    Google Scholar 

  34. Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.

    Google Scholar 

  35. Fradkin, A. (2017). Search, matching, and the role of digital marketplace design in enabling trade: Evidence from Airbnb.

  36. Gee, L.K., Jones, J.J., Fariss, C.J., Burke, M., & Fowler, J.H. (2017). The paradox of weak ties in 55 countries. Journal of Economic Behavior & Organization, 133, 362–372.

    Google Scholar 

  37. Goel, S., Mason, W., & Watts, D.J. (2010). Real and perceived attitude agreement in social networks. Journal of Personality and Social Psychology, 99(4), 611.

    Google Scholar 

  38. Goel, S., & Goldstein, D.G. (2013). Predicting individual behavior with social networks. Marketing Science, 33(1), 82–93.

    Google Scholar 

  39. Goes, P.B., Lin, M., & Au Yeung, C.-m. (2014). “popularity effect” in user-generated content: Evidence from online product reviews. Information Systems Research, 25(2), 222–238.

    Google Scholar 

  40. Granovetter, M.S. (1977). The strength of weak ties. In Social networks (pp. 347–367): Elsevier.

  41. Hartmann, W.R., Manchanda, P., Nair, H., Bothner, M., Dodds, P., Godes, D., Hosanagar, K., & Tucker, C. (2008). Modeling social interactions: identification, empirical methods and policy implications. Marketing Letters, 19(3-4), 287–304.

    Google Scholar 

  42. Hartmann, W.R. (2010). Demand estimation with social interactions and the implications for targeted marketing. Marketing Science, 29(4), 585–601.

    Google Scholar 

  43. Hosanagar, K., Fleder, D., Lee, D., & Buja, A. (2013). Will the global village fracture into tribes? recommender systems and their effects on consumer fragmentation. Management Science, 60(4), 805–823.

    Google Scholar 

  44. Huang, Y., Singh, P. V., & Ghose, A. (2015). A structural model of employee behavioral dynamics in enterprise social media. Management Science, 61 (12), 2825–2844.

    Google Scholar 

  45. Ibarra, H. (1992). Homophily and differential returns: Sex differences in network structure and access in an advertising firm. Administrative Science Quarterly, 422–447.

  46. Iyengar, R., Van den Bulte, C., & Valente, T. W. (2011). Opinion leadership and social contagion in new product diffusion. Marketing Science, 30(2), 195–212.

    Google Scholar 

  47. Jackson, M.O. (2008). Social and economic networks. Princeton University Press.

  48. Katona, Z., & Sarvary, M. (2008). Network formation and the structure of the commercial world wide web. Marketing Science, 27(5), 764–778.

    Google Scholar 

  49. Kossinets, G., & Watts, D. J. (2009). Origins of homophily in an evolving social network. American Journal of Sociology, 115(2), 405–450.

    Google Scholar 

  50. Kwon, H. E., Oh, W., & Kim, T. (2017). Platform structures, homing preferences, and homophilous propensities in online social networks. Journal of Management Information Systems, 34(3), 768–802.

    Google Scholar 

  51. Lerman, K., Jain, P., Ghosh, R., Kang, J.-H., & Kumaraguru, P. (2013). Limited attention and centrality in social networks. In 2013 International Conference on Social Intelligence and Technology (SOCIETY) (pp. 80–89): IEEE.

  52. Lewis, R. A., & Rao, J. M. (2015). The unfavorable economics of measuring the returns to advertising. The Quarterly Journal of Economics, 130(4), 1941–1973.

    Google Scholar 

  53. Lin, M., & Viswanathan, S. (2015). Home bias in online investments: an empirical study of an online crowdfunding market. Management Science, 62(5), 1393–1414.

    Google Scholar 

  54. Linkedin. (2016). People you may know. https://www.linkedin.com/help/linkedin/answer/29/people-you-may-know-feature-overview?lang=en.

  55. Mayzlin, D., & Yoganarasimhan, H. (2012). Link to success: How blogs build an audience by promoting rivals. Management Science, 58(9), 1651–1668.

    Google Scholar 

  56. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.

    Google Scholar 

  57. Mele, A. (2017). A structural model of dense network formation. Econometrica, 85(3), 825–850.

    Google Scholar 

  58. Mollica, K. A., Gray, B., & Treviño, L. K. (2003). Racial homophily and its persistence in newcomers’ social networks. Organization Science, 14(2), 123–136.

    Google Scholar 

  59. Moricz, M., Dosbayev, Y., & Berlyant, M. (2010). Pymk: friend recommendation at myspace. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (pp. 999–1002): ACM.

  60. Oestreicher-Singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. Mis Quarterly, 65–83.

  61. Oestreicher-Singer, G., Libai, B., Sivan, L., Carmi, E., & Yassin, O. (2013). The network value of products. Journal of Marketing, 77(3), 1–14.

    Google Scholar 

  62. Owen, A. B., Eckles, D., & et al. (2012). Bootstrapping data arrays of arbitrary order. The Annals of Applied Statistics, 6(3), 895–927.

    Google Scholar 

  63. Peng, J., Agarwal, A., Hosanagar, K., & Iyengar, R. (2018). Network overlap and content sharing on social media platforms. Journal of Marketing Research, 55(4), 571–585.

    Google Scholar 

  64. Phan, T. Q., & Airoldi, E. M. (2015). A natural experiment of social network formation and dynamics. Proceedings of the National Academy of Sciences, 112(21), 6595–6600.

    Google Scholar 

  65. Phan, T. Q., & Godes, D. (2018). The evolution of influence through endogenous link formation. Marketing Science, 37(2), 259–278.

    Google Scholar 

  66. Price, R. (2012). Growth hacking: leading indicators of engaged users. http://www.richardprice.io/post/34652740246/growth-hacking-leading-indicators-of-engaged.

  67. Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods & Research, 40(2), 211–239.

    Google Scholar 

  68. Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423 & 623–656.

    Google Scholar 

  69. Shi, Z., Rui, H., & Whinston, A. B. (2014). Content sharing in a social broadcasting environment: evidence from twitter. MIS Quarterly, 38(1), 123–142.

    Google Scholar 

  70. Shriver, S. K., Nair, H. S., & Hofstetter, R. (2013). Social ties and user-generated content: Evidence from an online social network. Management Science, 59(6), 1425–1443.

    Google Scholar 

  71. Su, J., Sharma, A., & Goel, S. (2016). The effect of recommendations on network structure. In Proceedings of the 25th international conference on World Wide Web (pp. 1157–1167): International World Wide Web Conferences Steering Committee.

  72. Sun, T., Viswanathan, S., & Zheleva, E. (2019). Creating social contagion through firm mediated message design: Evidence from a randomized field experiment. Management Science, Forthcoming.

  73. Sundararajan, A. (2007). Local network effects and complex network structure. The BE Journal of Theoretical Economics, 7,(1).

  74. Susarla, A., Oh, J. -H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from youtube. Information Systems Research, 23 (1), 23–41.

    Google Scholar 

  75. Thelwall, M. (2009). Homophily in myspace. Journal of the Association for Information Science and Technology, 60(2), 219–231.

    Google Scholar 

  76. Toubia, O., & Stephen, A. T. (2013). Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter? Marketing Science, 32 (3), 368–392.

    Google Scholar 

  77. Tucker, C. (2008). Identifying formal and informal influence in technology adoption with network externalities. Management Science, 54(12), 2024–2038.

    Google Scholar 

  78. Ugander, J., Backstrom, L., Marlow, C., & Kleinberg, J. (2012). Structural diversity in social contagion. Proceedings of the National Academy of Sciences, 201116502.

  79. Wang, C., Zhang, X., & Hann, I.-H. (2018). Socially nudged: A quasi-experimental study of friends’ social influence in online product ratings. Information Systems Research.

  80. Yadav, M. S., & Pavlou, P. A. (2014). Marketing in computer-mediated environments: Research synthesis and new directions. Journal of Marketing, 78(1), 20–40.

    Google Scholar 

  81. Yoganarasimhan, H. (2012). Impact of social network structure on content propagation: a study using youtube data. Quantitative Marketing and Economics, 10 (1), 111–150.

    Google Scholar 

Download references

Author information



Corresponding authors

Correspondence to Tianshu Sun or Sean J. Taylor.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Appendix A: Tables and Figures

Table 1 Summary Statistics Overall the experiment includes about 50 million viewer-profile pairs
Fig. 9

Distribution. We group all pairs by the number of actual things in common shared between the viewer-profile and draw its distribution. Most viewer-profile pairs have one thing in common

Fig. 10

Distribution. We group all pairs by the number of actual things in common shared between the pairs and draw separate distribution for pairs with and without mutual friend

Fig. 11

Distribution of things in common when the viewer-profile pair share two things in common. Among the large number of combinations between the five types of TIC (mutual city, like, hometown, education, work), we only show distributions for the nine most commonly seen combinations. The remaining combinations consist of a very small portion of our sample

Fig. 12

Distribution of profiles per viewer (left) and Distribution of viewers per profile (right)

Fig. 13

Effects of displaying things in common. Same as Fig. 6 with an additional panel for pairs with 3 TIC

Fig. 14

Effects of displaying things in common, for pairs with and without mutual friends. Same as Fig. 7 with an additional panel for pairs with 3 TIC

Table 2 Effect of Displaying TICs on Friend Requests and Formation RR stands for relative risk and is calculated using the relative ratio in means compared to the baseline group (i.e. viewer-profile pairs with the same number of actual TICs but zero TICs at display)
Table 3 Effect of Displaying TICs on Friend Requests and Formation (Decomposed by Whether View-Profile Pairs Share Mutual Friends) RR stands for relative ratio and is calculated using the relative lift in means compared to the baseline group (i.e. viewer-profile pairs with the same number of actual TICs but zero TICs at display)
Fig. 15

Acceptance Rate Conditional on Request

Fig. 16

Effect of Displaying Thing in common on Friend Request, for male viewers and female viewers

Fig. 17

Effect of Displaying Thing in common on Friend Request, for gender combinations of viewers and profiles

Fig. 18

Effect of Displaying Thing in common on Friend Request, for viewers with different number of friends

Fig. 19

Incremental Effect of displaying both things in Common, as compared to displaying one. Following information theory, we measured the incremental information (‘surprisal’) of displaying two TIC (mutual hometown and current city), as compared displaying one, using normalized pointwise mutual information (PMI). We calculate normalized PMI using its formal definition (Church and Hanks 1990): the surprisal (\(-\log _{2}(\Pr (X_{ia} = X_{ja}))\)) of displaying mutual hometown plus the surprisal of displaying current city minus the joint surprisal of displaying both mutual current city and hometown, normalized by joint surprisal. The PMI measure, ranging from + 1 to -1, captures the co-occurrence likelihood and is symmetric with respect to the two TICs. An PMI with + 1 indicates 100% likelihood of co-occurrence, thus displaying both mutual hometown and current city would reveal little additional information or ‘surprisal’. In contrast, a low PMI indicates a small likelihood of co-occurrence thus displaying both TIC would would be more surprising to the viewer given that they already know one of the TIC). The empirical finding is aligned with such mechanism and further confirms the belief update process underlying the effectiveness of displaying TIC. To generate smooth curves, we fit LOESS regressions separately for the display 2 TIC and display 1 TIC conditions for each of 500 bootstrap replicates clustered on viewer. The dashed lines are the 95% bootstrap confidence intervals for the smoothed estimates of the ratio

Fig. 20

Comparison of Post-friendship Interaction score between friendship ties in control versus treatment group. We categorize all the friendship ties formed in our experiment into two groups based on whether certain TIC is shown during friendship formation process, and then calculate the difference between the groups in the volume of their post-friendship interactions (like, comment, messaging). We do not find any significant difference in the post-friendship interactions between the control and treatment group

Fig. 21

Effects of showing zero, one, or both things in common when the viewer-profile pair share two things in common. Among the large number of combinations between the five types of TIC (mutual city, like, hometown, education, work), we only show the effect of TIC display for the nine most commonly seen combinations. The remaining combinations consist of a very small portion of our sample

Appendix B: Effect of displaying different types of things in common

The availability of multiple types of things in common in our experiment also enables us, for the first time, to investigate the relative importance of a range of TIC documented in the literature (McPherson et al. 2001) in the same context. Previous studies on homophily have separately documented different things in common in social networks (Ibarra 1992; Currarini et al. 2010), but never compared the causal effect of them within the same context.

We find a variation in the effect of showing different types of things in common (Fig. 22). We focus on presenting the results for viewer-profile pairs with only one thing in common, as they represent the majority of the sample (Fig. 6). There are three categories of things in common: past experience (mutual hometown, mutual education, mutual work), current context (mutual city), common interests (overlap of page like). As shown in Fig. 22, certain type of things in common, such as mutual likes, city and hometown, may lead to a larger increase in friendship formation than other types of things in common (mutual education). Previous studies on homophily has separately documented different type of things in common across different situations (Ibarra 1992; Currarini et al. 2010; Thelwall 2009), but never compared the relative importance of them within the same context. Similar to previous studies on social influence (Aral and Walker 2014), we find that social factors such as current context (e.g. mutual city) and past experience (mutual hometown) are associated with a large impact on friendship formation. However, we also find that showing overlap of interest may also be effective: a finding that has not been established before. In this way, we contribute to the literature on the role of different type of things in common (or similarity) in facilitating interpersonal interaction (Berscheid and Reis 1998).

Fig. 22

Distribution and effects of showing specific type of things in common when the viewer-profile pair share one thing in common. Using zero things in common as a baseline, showing shared likes and hometown significantly increases the relative risk of friendship formation, while we cannot reject the null hypothesis that showing a shared city or education causes any change. 95% confidence intervals are computed from standard errors estimated using a bootstrap clustered on viewer with 500 bootstrap replicates

Fig. 23

Effect of Specific Type of Thing in common, for pairs with and without mutual friends. We group all pairs with one thing in common by the specific things in common as well as the existence of mutual friends. Using pairs with zero things in common shown as a baseline within each group, we find showing certain thing in common increases the relative risk of friendship formation, but only for people with no mutual friends and for shared likes, hometown and city

Appendix C: Methodological: correlation from observational data is opposite to the causal effect of TIC

Finally, we want to demonstrate the importance and necessity of using a randomized experiment approach to identify causal effect of showing TIC. We explicitly compare the estimates from our experiment with those from a direct correlation analysis using observational data to highlight the advantage of our method. In Fig. 24, we can see that interestingly the correlation between number of things in common and friendship formation is in the opposite direction from the identified causal effect of showing things in common: for viewer-profile pairs with mutual friends, the observed correlation suggests a very positive and significant effect of showing more things in common, i.e. pairs with more TIC is more likely to form friendship, whereas the true causal effect of TIC based on experiment results is zero; for viewer-profile pairs with no mutual friends, the observed correlation suggests a zero or even slightly negative relationship between more TIC and friendship formation, whereas the true causal effect of showing TIC is large and significant. This contrast further highlights the importance of using a randomized experiment for causal inference. As shown above, using the observed correlation in the secondary data may significantly bias the effect of displayed things in common. We can cleanly identify the causal effect from correlation only by using a proper randomization.

Fig. 24

Correlation vs Causal Effect of TIC on friendship formation. Correlation between number of things in common in a pair and the friendship formation rate (Left) is biased and opposite to the true causal effect of showing things in commons (middle and right)

Appendix D: Theoretical Contribution: Identifying the Role of Individual Preference in Things in Common (versus Structural Factor) in Network Formation

Besides practical importance, our findings from a carefully designed large-scale randomized experiment may contribute to social network literature and improve our theoretical understanding of the origin of network formation and homophily, in a few ways.

First, while strategic network formation have been studied in detail using analytical models (Bala and Goyal 2000; Jackson 2008) and simulations (Phan and Godes 2018), empirical work designed to test theories and examine the drivers of network formation is still in their infancy. Most of the empirical works on network formation (Currarini et al. 2010; Mele 2017) impose strong functional assumptions and use network structure for identificationFootnote 21. To the best of our knowledge, our study is the first randomized field experiment to directly engineer the drivers of network formation (i.e. viewer’s information about the alter as in our case). Our experiment design and framework provides a new way to examine various factors underlying network formation and the relative importance between them, in real networks and at a very large scale.

Second, as documented by a broad history of literature on homophily in social network (McPherson et al. 2001), an individual is more likely to form a friendship tie with someone that is similar to her/him in demographic and behavioral attributes (Centola 2015; Ameri et al. 2017; Goel and Goldstein 2013). However, the origin of homophily is far from clear (Kossinets and Watts 2009; Currarini et al. 2010). Recent literature hypothesizes that the homophily pattern in social network might be partially driven by individuals’ preference over similar others (termed as preference or choice homophily Currarini et al.2009), as opposed to the structural factors. However, as far as we know, no study has provided clear empirical evidence on whether and when preference for TIC would drive network formation. As the literature repeatedly acknowledge (Currarini et al. 2010; Phan and Airoldi 2015), such lack of insights is because preference factor and structural factor in network formation process are inherently confounded in observational data and extremely hard to disentangle: pairs with similar attributes are always more likely to have mutual friends. We address the challenge by designing a experiment to exogenously vary the prominence of things in common displayed during friendship formation in a real social network. The randomization is independent of structural factor therefore allows us, for the first time, to demonstrate the importance of preference in driving friendship formation. Third, we examine the relationship between preference and structural factor in network formation and find that preference over TIC matters in the absence of structural factor (mutual friend) thus can be leveraged to encourage formation of weak ties.

Finally and importantly, the variation in the treatment effect within each type of TIC (e.g. sharing mutual hometowns of different size) sheds light on the underlying mechanisms: aligned with belief update process (Ely et al. 2015), displaying TIC is more effective when the information shown is less expected, and the effect size of a certain TIC is in proportion to the ‘surprise’ it creates to the viewer (measured by bits in information theory). Our study is among the first to understand network formation and homophily from a belief update perspective. SN sites can use our information-theoretic principles to optimally select specific things in common to highlight in online friendship formation process. The information-theoretic framework may also be extended to guide the personalized design of user profile for each viewer-profile interaction.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sun, T., Taylor, S.J. Displaying things in common to encourage friendship formation: A large randomized field experiment. Quant Mark Econ 18, 237–271 (2020). https://doi.org/10.1007/s11129-020-09224-9

Download citation


  • Network formation
  • Social interactions
  • Field experiment
  • Things in common
  • Homophily
  • Information theory
  • Diversity

JEL Classification

  • D85
  • Z13