Online health communities play an increasingly important role in many people’s lives. Here, people ask for and get for information, help, and advice about illnesses affecting themselves and their loved ones.
In addition, people receive vital emotional support. For many, this has a significant impact on their ability to cope at a time that may be the most traumatic of their lives.
But while it’s straightforward to measure the amount of information a community provides by counting visitors, posts, and replies, it’s much harder to judge the value of emotional support.
It’s even harder to identify users who provide this support most effectively. These people are hugely important because of the vital role they play in influencing the emotional state of other community users.
Which is why the work of Kang Zhao at the University of Iowa and a few pals is impressive. Today, these guys reveal how they have automatically identified influential members of an online health network called the Cancer Survivors Network.
Kang and co studied 500,000 anonymised posts, organised into 50,000 threaded discussions that took place between 2000 and 2010. Their hypothesis is that conversations can change the emotional state of the person who started the discussion and that this ought to be reflected in the emotional content of this person’s posts.
So they set out to examine the emotional content of the initial post in the discussion and see how later comments by the same person changed.
They began by manually tagging 300 randomly selected posts as either positive or negative. An example of a post with negative sentiment is: “My mom became resistant to chemo after 7 treatments and now the trial drug is no longer working :(, ” An example of a post with positive sentiment is: “Hooray! The tumor is gone, according to my doctor!”
They then extracted features useful in identifying the sentiment, such as the presence of smileys, exclamation marks, and words with positive or negative connotations. They used these features to train software classifiers to automatically spot positive or negative posts.
They then ran the classifier on the entire set of threads that had at least one reply and then one self-reply from the originator.
The results make for interesting reading. Kang and co found that the sentiment in the first self-reply is generally significantly more positive than the original post. After that, self-replies become gradually more positive.
That seems to confirm the hypothesis that other users can significantly alter the emotional state of the originator.
This also allowed Kang and co to see which users triggered the change. They looked only at the replies published before the self-replies, and they also concentrated on quick replies to rule out the possibility that other factors might be responsible for the change, such as a change in health.
Kang and co then used this data to work out which users were the most influential: in other words, which people provided the biggest and most consistent emotional boosts.
Of course, it’s possible to use traditional methods such as PageRank to find important users in a network. But Interestingly, Kang and co say these methods do not necessarily find the individuals who provide the most significant emotional support.
In effect, what they have found is an entirely new way to rank members of a community based on their ability to help others—a kind of good Samaritan index.
That could turn out to be very important for these kinds of networks. Knowing who the best Samaritans are and when they leave the community (perhaps through death) is important for judging the utility of the community and how it is changing. As is the ability to spot when new Samaritans that are becoming influential.
Clearly these networks provide an important social service. So any way that this service can be maintained and even improved is surely of great value.
Ref: arxiv.org/abs/1211.6086: Finding Influential Users Of An Online Health Community: A New Metric Based On Sentiment Influence
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