Complex contagions and the diffusion of popular Twitter hashtags in Nigeria


Social media sites such as Facebook and Twitter provide highly granular time-stamped data about the interactions and communications between people and provide us unprecedented opportunities for empirically testing theory about information flow in social networks. Using publicly available data from Twitter’s free API (Application Program Interface), we track the adoption of popular hashtags in Nigeria during 2014. These hashtags reference online marketing campaigns, major news stories, and events and issues specific to Nigeria, including reactions to the kidnapping of 276 schoolgirls in Northeastern Nigeria by the Islamic extremist group Boko Haram. We find that hashtags related to Nigerian sociopolitical issues, including the #bringbackourgirls hashtag, which was associated with protests against the Nigerian government’s response to the kidnapping, are more likely to be adopted among densely connected users with multiple network neighbors who have also adopted the hashtag, compared to mainstream news hashtags. This association between adoption threshold and local network structure is consistent with theory about the spread of complex contagions, a type of social contagion which requires social reinforcement from multiple adopting neighbors. Theory also predicts the need for a critical mass of adopters before the contagion can become viral. We illustrate this with the #bringbackourgirls hashtag by identifying the point at which the local social movement transforms into a more widespread phenomenon. We also show that these results are robust across both the follow and reply/mention/retweet networks on Twitter. Our analysis involves data mining records of hashtag adoption and of the social connections between adopters.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    For simplicity, Centola and Macy used an absolute threshold expressed in terms of the absolute number of adopting neighbors, but their results hold also for relative thresholds expressed in terms of the fraction of adopting neighbors.

  2. 2.

    The collection of data from Nigeria was related to a previously funded effort, unrelated to this work.

  3. 3.

    It should be noted that spammers may also use popular hashtags to gain attention for their tweets. Since we mostly focus on the early part of a tag’s lifespan, we assume that this effect is negligible. Also, there are likely many tweets about the topic of the hashtag that do not contain the hashtag. While some data will be missed in ignoring these tweets, the hashtag convention is so pervasive on Twitter that we assume the lower recall resulting from our retrieval strategy will not alter the results drastically.

  4. 4.

    Note that these fields contain the same information, but with reversed ordering of the coordinates.

  5. 5.

    Only 1 % of users had geotags in their profile strings and are excluded from this discussion.

  6. 6.

    In the case where the total days examined D is less than 10, we use the first D days.

  7. 7.

    News stories and dates were obtained via a Lexis/Nexis search.


  1. Barash V (2011) The dynamics of social contagion. PhD thesis, Cornell University

  2. Barash V, Cameron C, Macy M (2012) Critical phenomena in complex contagions. Soc Netw 34(4):451–461

    Article  Google Scholar 

  3. BBC (2014) #BBCtrending: The man who 'disappeared' in Nigeria - #FreeCiaxon. Retrieved from Accessed 23 Dec 2015

  4. Centola D, Macy M (2007) Complex contagions and the weakness of long ties1. Am J Sociol 113(3):702–734

    Article  Google Scholar 

  5. Geonames (2015) Retrieved from Accessed 23 Dec 2015

  6. Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 8(6):1420–1443

    Article  Google Scholar 

  7. International Telecommunications Union (2015) Statistics: percentage of individuals using the internet. Retrieved from Accessed 23 Dec 2015

  8. Know Your Meme (2015) #WeAreAllMonkeys. Retrieved from Accessed 23 Dec 2015

  9. Leskovec J, Backstrom L, Kleinberg J (2009) Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 497–506. ACM

  10. Marwell G, Oliver PE, Prahl R (1988) Social networks and collective action: a theory of the critical mass. iii. Am J Sociol 502–534

  11. Milgram S (1967) The small world problem. Psychol Today 2(1):60–67

    MathSciNet  Google Scholar 

  12. Nahon K, Hemsley J  (2013)  Going viral. Polity Press, Cambridge

  13. Odufuwa F (2012) Understanding what is happening in ICT in Nigeria. Technical report, Research ICT Africa

  14. Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web, pp 695–704. ACM

  15. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442

    Article  Google Scholar 

  16. Weng L, Menczer F, Ahn YY (2013) Virality prediction and community structure in social networks. Scientific reports, 3

  17. Wikipedia (2014) YesAllWomen. Retrieved from Accessed 23 Dec 2015

  18. Wu S, Tan C, Kleinberg J, Macy M (2011) Does bad news go away faster? In: Fifth international AAAI conference on weblogs and social media ICWSM. AAAI

Download references


This work was funded by the United States Air Force Office of Scientific Research (AFOSR) through the Minerva Initiative under Grant FA9550-15-1-0036.

Author information



Corresponding author

Correspondence to Vladimir Barash.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fink, C., Schmidt, A., Barash, V. et al. Complex contagions and the diffusion of popular Twitter hashtags in Nigeria. Soc. Netw. Anal. Min. 6, 1 (2016).

Download citation


  • Complex contagion
  • Social movements
  • Network structure
  • Twitter