“We have a list of terms that we’re pulling for Twitter and trying to evolve those,” Olsen said in an interview last October.
Olsen said one of the biggest challenges was being able determine false spikes in mention of illnesses like flu on Twitter. False spikes occur when there is a large amount of mentions that don’t necessarily coincide with increased prevalence of an illness.
“Both Kim Kardashian and Miley Cyrus had the flu the same week in April and it really threw off everything as far as our numbers go,” she said.
People are likely to tweet about the flu when they get the vaccine or when talking about a news article. These mentions contribute to false spikes.
She also noted that people with gastrointestinal illnesses or illnesses related to food poisoning may be more likely to tweet about the food or the restaurant where they ate than the illness.
“This is an area that will continue to evolve for as long as Twitter is used as a data source,” Kushner said. “Some ways to counteract the ‘celebrity effect’ and other false spikes is to use qualifier terms linked to the taxonomy, to eliminate retweets from your numbers, and other smart filtering techniques.” All of the competitors in the challenge did a good job of “filtering through the noise to get to the heart of the data,” she said of the contest.
The greatest successes of Twitter monitoring came during the H1N1 pandemic. ASPR used Twitter to identify school closures by verifying user reports with official sources. “At a federal level, there is no way to see these school closures,” Olsen said in last year’s interview. “What we have seen is that the stream may not always be useful for getting a spike in flu, but it would be useful in getting specific questions related to flu.”
ASPR is looking for a way to combine streams of data from electronic health records, social media, and news to find consistency across all three during a disaster.
“That would give us some understanding of where we can have more confidence in each of those streams,” she said.
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