|Title||Discovering Explanatory Models to Identify Relevant Tweets on Zika|
|Publication Type||Conference Paper|
|Year of Publication||2017|
|Authors||Roopteja Muppalla, Michele Miller, Tanvi Banerjee, William Romine|
|Conference Name||39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)|
|Conference Location||Jeju Island, Republic of Korea|
Zika virus has caught the worlds attention, and has led people to share their opinions and concerns on social media like Twitter. Using text-based features, extracted with the help of Parts of Speech (POS) taggers and N-gram, a classifier was built to detect Zika related tweets from Twitter. With a simple logistic classifier, the system was successful in detecting Zika related tweets from Twitter with a 92% accuracy. Moreover, key features were identified that provide deeper insights on the content of tweets relevant to Zika. This system can be leveraged by domain experts to perform sentiment analysis, and understand the temporal and spatial spread of Zika.
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