Importance Of Social Media In Public Health

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One domain where data has grown by massive proportions in recent years, and continues to grow, is social media. Social networks have seen an unprecedented growth in terms of users worldwide (e.g., as of 11th July 2014, Twitter has over 645,750,000 users and grows by an estimated 135,000 users every day, generating 9100 tweets per second). A large population of patients are actively involved in sharing and posting health related information in social media and particularly health social networks. A recent survey by the Pew Research Center’s survey has elucidated the relevance of social media in modern day public health, explaining that 34% of caregivers and 20% of patients read or watch someone else’s
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Recent advances in the data processing capabilities of machines, and machine learning and NLP research present the possibility of utilizing this massive data source for a variety of purposes, including public health. The fact that it is a direct source of users’ personal experiences makes it a lucrative resource. According to Harpaz et al, social media offers new opportunities for public health monitoring due to the availability of large amounts of data that is internet-based, patient-generated, unsolicited, and up-to-date. The use of social media for health-related and other tasks is, however, not without drawbacks and difficulties. The drawbacks found when utilizing he user generated content of social media may include issues with the credibility, recency, uniqueness, frequency, and salience of the data. Abbasi and Adjeroh demonstrate the potential downside of each of these five points and the importance of selecting the right media channel for social media analytics. For example, the authors discuss the potential low salience of Twitter because of the short text limits. In addition to these general problems related to the data generated within social media, there are difficulties and challenges posed by the processing and extraction of relevant information using NLP techniques. A frequently encountered challenge is due to the fact that the data is generated by consumers, and they tend to use misspellings, non-medical, descriptive terms to discuss health issues. This reduces a system’s ability to automatically extract mentions of relevant concepts and map them to suitable medical lexicons for further analysis. Traditional NLP methods that are used on longer texts have proven to be inadequate when applied to short texts, such as those found in Twitter. Thus, recent research tasks have focused on developing NLP tools specifically for data from social media. Some recent research has reported the imbalance
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