Selected article for: "better process understand and process understand"

Author: Thiébaut, Rodolphe; Thiessard, Frantz
Title: Artificial Intelligence in Public Health and Epidemiology
  • Document date: 2018_8_29
  • ID: xnfrulbf_9
    Snippet: The other topic covered by several of the papers selected by the review process concerned the analysis of online social media to better understand the attitudes and beliefs toward a given topic, such as vaccination. A social network analysis of Twitter messages ("tweets") revealed a semantic network for positive, negative, and neutral vaccine sentiment [9] . Beyond this type of analysis, it is fruitful to predict and understand the dynamics of va.....
    Document: The other topic covered by several of the papers selected by the review process concerned the analysis of online social media to better understand the attitudes and beliefs toward a given topic, such as vaccination. A social network analysis of Twitter messages ("tweets") revealed a semantic network for positive, negative, and neutral vaccine sentiment [9] . Beyond this type of analysis, it is fruitful to predict and understand the dynamics of vaccinating behavior. In another paper, Pananos et al. modeled the interaction between vaccination decisions and disease dynamics where one influences another in a nonlinear feedback loop [17] . They used the theory of critical transitions to derive indicators that may help public health officials anticipate when resistance to vaccination might develop and intensify. They applied their approach to data from tweets and Google searches around the Disneyland measles outbreak that occurred in 2015 in California [17] . One of the two best papers described below, analyzed the relationship between Middle East Respiratory Syndrome (MERS), mass media, and public emotions during an outbreak in 2015 in Korea [7] .

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