If you’re happy and we know it … are your friends?

Do your friends influence your behavior?  Of course they do.  But it’s hard to actually measure their influence.  Social contagion is difficult to distinguish from homophily, the tendency we have to seek relationships with people like ourselves.

In response to the “happiness is contagious” phenomenon promoted by Nicholas Christakis and James Fowler, we here at onehappybird were wondering whether happy Twitter users were more likely to be connected to each other.  In other words, is happiness assortative in the Twitter social network?  (See related work here.)

In the image below, each circle represents a person in the social network of the center node.  We color nodes by the happiness of their tweets during a single week.  Pink colors are happier, gray colors are sadder, and nodes depicted with the color black did not meet our thresholding criteria (50 labMT words).

We established a friendship link between two users if they both replied directly to the other at least once during the week.

As users are added to this network, it quickly becomes difficult to tell whether pink nodes are disproportionately connected to each other, so instead we look at the correlation of their happiness scores.  The plot below shows the Spearman correlation coefficient of the happiness ranks for roughly 100,000 people, with blue squares and green diamonds indicating different word thresholds, and red circles representing the same network but with randomly shuffled happiness scores.

The larger correlation for friends indicates that happy users are likely to be connected to each other, as are sad users. Moving further away from one’s local social neighborhood to friends of friends, and friends of friends of friends, the strength of assortativity decreases as expected.

We also looked at the average happiness of users as a function of their number of friends (degree k). Happiness increases gradually with popularity, with large degree nodes demonstrating a larger average happiness than small degree nodes.

The most popular users used words such as “you,” “thanks,” and “lol” more frequently than small degree nodes, while the latter group used words such as “damn,” “hate,” and “tired” more frequently.  The transition appears to occur near Dunbar’s number (around 150), demonstrating a quantitative difference between personal and professional relationships.

Finally, here we show a visualization of the reciprocal-reply network for the day of October 28, 2008.

The size of the nodes is proportional to their degree, and colors indicate communities detected by Gephi’s community detection algorithm.

For more details, see the publication:

C. A. Bliss, I. M. Kloumann, K. D. Harris, C. M. Danforth, P. S. Dodds.  Twitter Reciprocal Reply Networks Exhibit Assortativity with Respect to Happiness. Journal of Computational Science. 2012. [pdf]

Abstract: Based on nearly 40 million message pairs posted to Twitter between September 2008 and February 2009, we construct and examine the revealed social network structure and dynamics over the time scales of days, weeks, and months. At the level of user behavior, we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression. We find users’ average happiness scores to be positively and significantly correlated with those of users one, two, and three links away. We strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness. We also find evidence that more well connected users write happier status updates, with a transition occurring around Dunbar’s number. More generally, our work provides evidence of a social sub-network structure within Twitter and raises several methodological points of interest with regard to social network reconstructions.



Filed under psychology, social phenomena

38 responses to “If you’re happy and we know it … are your friends?

  1. The Smile Scavenger

    Thank you for sharing the results of this study. I am quite pleased to see concrete support for these ideas. Keep up the good work!

  2. So, I realize you folks are pretty serious about all these plots, graphs, pies and charts (you are serious, aren’t you?) I just want to say the October 28 visualization certainly makes me happy in its color choices and random(ish) splatitude, and anyone reading this comment will now know that about me. Congrats on fresh press, and making me happy today.

  3. Interesting replication and data.

  4. Can”t you simplify this for a layman?

  5. Rogue Honeybadger

    Great blog!!! Very informative! Keep up the good work! 🙂

  6. I have the joy of the Lord! Does that count?????

  7. Fascinating! But I guess the only caveat I’d suggest is that the people are “perceived” as happy/sad rather than being happy or sad — or at least they depict themselves as such through their words. I think there often is a sizable disconnect between the way one promotes themselves as being via social networks versus the way one is IRL.

    But still, fascinating…


  8. There is too much craziness in this study to be of interest. I do like the patterns and colors, they make some artsy looking eye candy.

  9. What’s causing that dent on the left side of the last graphic?

  10. There is something to be said about the differences between words we choose to use publicly on social media (where they are broadcast and stored forever) and those we would use in private texts or voice conversations. As phone texting language usage crosses over into tweets and the like, mostly due to the seamlessness between the two realms now afforded by smartphones and tablets, it would be interesting to see this type of analysis done again with more current tweets to see if people are becoming more honest about their state of mind (ie. less guarded) in terms of how ‘happy’ or ‘sad’ their tweets might sound, disregarding how the stigma of sounding ‘sad’ may affect their popularity. Texting is very one-on-one private, while tweeting started out as explicitly and obviously public. As more private-like conversations seep onto Twitter, perhaps then the results of a study like this will be more telling of the Twitter moods it ambitiously has sought to reveal.

  11. sonali

    Such brilliant explanation! simply WOW!!! 🙂 great job, well-articulated.

  12. LOVE this post! Jumping for joy over here 🙂

  13. hahahaha.. Grumpa Joe i totally agree with you that it does make some artsy looking eye candy!!! but it is an amazing study, iv learned so much!! 😀

  14. It would seem that the branch of the social network of the human mind is on pace with current tech. sites like twitter and facebook are collectively expanding our social out reach to what the human mind is accustomed to.

  15. I’ve always been interested in the social psych phenomena that happy people tend to attract other happy people, which in turn makes them happier. It’s like the rich get richer…haha

    I think the word groups were particularly telling: “lol”, “thanks”, and “you” versus “hate”, “damn”, and “tired.” Makes you think twice about what you’re putting out into the universe when you tweet or post. Even if you’re not feeling particularly sunny, sending out a negative tweet or post may actually be promoting a pessimistic online presence.

  16. I have to agree with the study sited in the first 2 paragraphs. It has long been a belief of mine that about spreading joy or happiness from one person to another. A smile is contagious as they say.

  17. Cool Graphics! As a new blogger I can appreciate this post! I agree happiness is contagious… So is sneezing! AcHOOOooO!

  18. As a new blogger I can appreciate this post ! The colors are eye popping and the content was informative and creative! I agree that happiness is contagious, and so is sneezing! AcHoOOoo! 😀

  19. Fascinating studies! If this is true, someone out there needs to start a dating company that matches people with similar tweet personalities!

  20. Great study 🙂 lol Makes you really think Thanks 😉 hahahaha

  21. Without studying your figures and presentation in detail, I guess you’re saying we pass our current moods and emotions on from one to another.

    Yep, we sure do.

    If you’re using oxygen, be happy !!!

    It sure beats the alternative.

  22. So how did you quantify happiness?

  23. I must admit that there are some parts of this I do not fully understand. But it is interesting to consider Mikalee’s point that the way people express themselves online is not always how they are feeling…sometimes if I feel incredibly sad, I will leave even nicer replies to comments, or status updates, simply because I do not want to bring others down with my sadness. I would like to see a brain-scan study of what people feel in their brains when they type “lol”, and to test various generations of people- the youngsters versus the elders versus the people who rarely go online…personally, I never use “lol”. Ever. Unless it is sarcastic, or talking about a study on lol-use. 🙂 Great article though, glad you got on the first page so that I could see you!

  24. I liked this examination a lot. I was recently thinking about the expressed happiness / sadness and the unexpressed feelings which we can hardly know through only a screen… Can “lol”, “thanks” or such affirmative expressions be the determining indicators of happiness? I hardly believe it…
    But still, I liked this study and find it useful for a further detailed study in the future. Good work!

  25. Feelings on social networking sites are often exaggerated to attract a response.
    Interesting post though

  26. This is so interesting! I’ve always thought that happy people find happy people, but never thought about how that would translate into social media. Thanks for sharing!


  27. Nancy Turner

    Complex methods of data presentation, admittedly, it took me a few moments to work out what they were showing. But this is a great post and highly thought-provoking, thank you! 🙂


  28. Really interesting, thanks for sharing!

  29. Really lovely research and well written! I totally agree with it though. Also not only are more popular people happy ones, I think happy people become more popular than those who constantly post negative updates.

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