Wednesday, September 04, 2019

Communities, Bots and Vaccinations

Following on from our work on bots and health discussions in relation to online social networks (OSNs), Xiaoyi Yuan, Ross Schuchard and myself have just published a paper entitled "Examining Emergent Communities and Detecting Social Bots within the Polarized Online Vaccination Debate in Twitter" in Social Media + Society. Within the paper we explore the communication patterns of vaccine discussions in Twitter. More specifically we ask three questions:
  1. Do vaccine discussions on Twitter show a highly clustered pattern in the sense that users tend to communicate more often with those who have same opinions towards vaccination than those who do not? 
  2. If the communication is highly clustered, to what extent do pro-vaccine users reach out to anti-vaccine users and vice versa? 
  3. How much do social bots, computer algorithms designed to mimic human behavior and interact with humans in an automated fashion, contribute to the conversation as previous research has shown that social bots can have certain impact on human communication in social media?
In order to answer these questions, we use a variety of machine learning techniques (e.g.  logistic regression, support vector machine (e.g. linear and non-linear kernel), k-nearest neighbors, nearest centroid, and Na├»ve Bayes) trained with labeled data (which is available at https://github.com/XiaoyiYuan/vaccination_online_discussion) to categorize each user’s vaccination stance. By exploring a combination of opinion groups and retweet networks we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. In addition, our bot analysis (using the  open-source DeBot detection platform) discovered that 1.45% of the corpus users were identified as likely bots and these produced 4.59% of all tweets within our data set. If you wish to find out more about our paper, below you can read the abstract along with seeing some figures including a sketch of our methodology, a selection of results and a link to the paper.

Abstract:
Many states in the United States allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine can have direct consequences in public health. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ efforts of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining momentum. Studies have shown that bots on social media (i.e., social bots) can influence opinion trends by posting a substantial number of automated messages. The research presented here investigates the communication patterns of anti- and pro-vaccine users and the role of bots in Twitter by studying a retweet network related to MMR vaccine after the 2015 California Disneyland measles outbreak. We first classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups using supervised machine learning. We discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group. In addition, our bot analysis discovers that 1.45% of the corpus users were identified as likely bots which produced 4.59% of all tweets within our dataset. We further found that bots display hyper-social tendencies by initiating retweets at higher frequencies with users within the same opinion group. The article concludes that highly clustered anti-vaccine Twitter users make it difficult for health organizations to penetrate and counter opinionated information while social bots may be deepening this trend. We believe that these findings can be useful in developing strategies for health communication of vaccination.

Keywords: Anti-vaccine Movement, Twitter, Social Media, Opinion Classification, Bot Analysis




Full Reference:
Yuan, X.,  Schuchard, R.  and Crooks, A.T. (2019), Examining Emergent Communities and Detecting Social Bots within the Polarized Online Vaccination Debate in Twitter, Social Media + Society. https://doi.org/10.1177/2056305119865465 (pdf)

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