tetano
Editor, Senior Moderator
J Med Internet Res. 2017 Sep 12;19(9):e315. doi: 10.2196/jmir.7393.
[h=1]Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.[/h] Kagashe I[SUP]1[/SUP], Yan Z[SUP]1,[/SUP][SUP]2[/SUP], Suheryani I[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques.
[h=4]OBJECTIVE:[/h] Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance.
[h=4]METHODS:[/h] From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs' tweets using latent Dirichlet allocation (LDA).
[h=4]RESULTS:[/h] Our proposed classifier obtained an F[SUB]1[/SUB] score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks.
[h=4]CONCLUSIONS:[/h] The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases.
[h=4]KEYWORDS:[/h] Twitter messaging; disease outbreaks; influenza; influenza vaccines; machine learning; natural language processing; public health surveillance; social media
PMID: 28899847 DOI: 10.2196/jmir.7393
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[h=1]Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.[/h] Kagashe I[SUP]1[/SUP], Yan Z[SUP]1,[/SUP][SUP]2[/SUP], Suheryani I[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques.
[h=4]OBJECTIVE:[/h] Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance.
[h=4]METHODS:[/h] From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs' tweets using latent Dirichlet allocation (LDA).
[h=4]RESULTS:[/h] Our proposed classifier obtained an F[SUB]1[/SUB] score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks.
[h=4]CONCLUSIONS:[/h] The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases.
[h=4]KEYWORDS:[/h] Twitter messaging; disease outbreaks; influenza; influenza vaccines; machine learning; natural language processing; public health surveillance; social media
PMID: 28899847 DOI: 10.2196/jmir.7393
Free full text