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How tweets influence millennials decoded



Tweets, either for or against, can influence the thinking process of young people and the speed of the messenger matters here too, say researchers.
“Twitter is an important outlet. We know that,” said lead author Joseph Erba, Assistant Professor at the University of Kansas.
“We also know from traditional advertising and marketing literature that the visual identification of the communicator matters. What we were interested to see is if the visual identification of a Twitter user influences how people perceive the message. It does.”
Those trying to reach millennials on Twitter should keep in mind the race and the identity matter, and that conscious and unconscious responses were different, suggesting self-reported data should always be viewed cautiously. Finally, the messenger can be just as important as the message, the findings revealed.
“If you want a message to hit home with white millennials, you have to think not only about the message but who is delivering the message,” Erba said. “There needs to be a ‘match up’ between the topic discussed and the perceived identity of the spokesperson.”
The team took a sample of how white millennial participants viewed real tweets, then answered questions about their perceptions of the issue and about who tweeted the messages. Eye-tracking equipment mapped the time participants spent reading each post, used as a proxy for their attention to the tweets.
According to the eye-tracking data, participants looked longer at messages from white Twitter users, while self-reported data showed they would be more likely to engage with black Twitter users on the topic. Four little tweets were enough to significantly change their views, the results showed.  The study will be presented at the International Communication Association conference in May.


Google web searches can help predict Covid hotspots




By analysing Google web searches for keywords related to Covid-19, researchers have said that web-based analytics have demonstrated their value in predicting the spread of the infectious disease.

According to the study, published in the journal Mayo Clinic Proceedings, Strong correlations were found between keyword searches on the internet search engine Google Trends and Covid-19 outbreaks in parts of the US.

These correlations were observed up to 16 days prior to the first reported cases in some states.

“Our study demonstrates that there is information present in Google Trends that precedes outbreaks, and with predictive analysis, this data can be used for better allocating resources with regards to testing, personal protective equipment, medications and more,” said study author Mohamad Bydon from Mayo Clinic — a health care company in the US.

“Looking at Google Trends data, we found that we were able to identify predictors of hotspots, using keywords, that would emerge over a six-week timeline,” Bydon added.

Several studies have noted the role of internet surveillance in early prediction of previous outbreaks such as H1N1 and Middle East respiratory syndrome.

There are several benefits to using internet surveillance methods versus traditional methods, and this study says a combination of the two methods is likely the key to effective surveillance.

The study searched for 10 keywords that were chosen based on how commonly they were used and emerging patterns on the internet and in Google News at that time.

The keywords were: Covid symptoms, Coronavirus symptoms, sore throat, shortness of breath+fatigue+cough, coronavirus testing centre, loss of smell, antibody, face mask and more.

Most of the keywords had moderate to strong correlations days before the first Covid-19 cases were reported in specific areas, with diminishing correlations following the first case.

“Each of these keywords had varying strengths of correlation with case numbers,” said Bydon.

If we had looked at 100 keywords, we may have found even stronger correlations to cases. As the pandemic progresses, people will search for new and different information, so the search terms also need to evolve,” Bydon added.

The use of web search surveillance data is important as an adjunct for data science teams who are attempting to predict outbreaks and new hotspots in a pandemic.

“Any delay in information could lead to missed opportunities to improve preparedness for an outbreak in a certain location,” the authors noted.

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