An Effective Approach to Track Levels of Influenza-A (H1N1) Pandemic in India Using Twitter

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

“…In the former, the identified literature highlights issues such as the noisy nature of data from social media sites ( Al-garadi, et al, 2016 ; K. Ali et al, 2017 , Almazidy et al, 2016 , Chaudhary and Naaz, 2017 , Gayo-Avello et al, 2013 ) or insufficiency ( Pollacci et al, 2017 , Seltzer et al, 2017 , Zarrad et al, 2014 ). Other authors highlighted data processing issues with regard to the irrelevancy of data (K. Ali, et al, 2017 ) or the premise that data have different types and formatting styles ( Al-garadi, et al, 2016 ; K. Ali et al, 2017 , Chaudhary and Naaz, 2017 ; Jain & Kumar, 2018; Jain & Kumar, 2015 ). Aside from other scholars who agreed that processing data from social media requires excellent processing skills ( Gayo-Avello, et al, 2013 ), the need for a high volume of analysis procedures is acknowledged ( Al-garadi et al, 2016 , Chaudhary and Naaz, 2017 ; Jain & Kumar, 2018; Zarrad, et al, 2014 ).…”

“…Ali et al, 2013 , Chaudhary and Naaz, 2017 ). This approach can provide a rapid and effective monitoring mechanism for public health on a large scale at a low cost ( Al-garadi et al, 2016 , Choi et al, 2017 , Culotta, 2010 , Jain and Kumar, 2015 ; Pastor, 2020). Among the most well-known social media websites that are highly utilised in sentiment analysis are Facebook, Twitter, Reddit, Instagram, and news forums (K. Ali, et al, 2017 ).…”

“…For news sharing, predictability is acknowledged due to the capability of sentiment analysis by sharing news about diseases and outbreaks via social media ( Almazidy, et al, 2016 ); similarly, other emergencies (K. Ali, et al, 2017 ; S. Li, et al, 2020 ) can be easily shared via social media. For policies, governments can largely subject social media to sentiment analysis to control an epidemic ( Jain & Kumar, 2015 ; Pastor, 2020; Raamkumar, et al, 2020 ) and gain adequate time to act ( Chaudhary & Naaz, 2017 ; S. Li, et al, 2020 ) towards handling epidemic outbreaks by promptly issuing health advisories ( Chaudhary & Naaz, 2017 ; S. Li, et al, 2020 ), such as social distancing and satisfactory hygiene habits ( Raamkumar, et al, 2020 ), or even making decisions to handle emergency accident cases ( Deng, et al, 2015 ; L. Li et al, 2020 , Seltzer et al, 2017 ).…”

“…People showed other negative emotions ( anxiety, depression and indignation ) and less positive emotions after the declaration of COVID-19 . Another study by Jain and Kumar (2015) proposed a sentiment analysis method to track N1H1 in India by using Twitter content. Sentiment analysis and count-based techniques were applied to data sets that contain 91,495 tweets and queries to examine important issues relevant to the presence of N1H1 symptoms.…”

The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

“…Twitter has been used to examine a range of health behaviors, including e-cigarettes and tobacco use (Aphinyanaphongs, Lulejian, Brown, Bonneau, & Krebs, 2016;Jo, Kornfield, Kim, Emery, & Ribisl, 2015;Myslín, Zhu, Chapman, & Conway, 2013;Sofean & Smith, 2013;Step, Bracken, Trapl, & Flocke, 2016); drug use and abuse (Cavazos-Rehg, Krauss, Grucza, & Bierut, 2014;Daniulaityte et al, 2015;Hanson et al, 2013;Katsuki, Mackey, & Cuomo, 2015); suicide (O'Dea et al, 2015); and HPV vaccination (Zhou et al, 2015). It has also been used to study a range of physical health outcomes (Aslam et al, 2014;Bosley et al, 2013;Broniatowski, Paul, & Dredze, 2013;Chew & Eysenbach, 2010;Chorianopoulos & Talvis, 2015;Collier, Son, & Nguyen, 2011;Fung et al, 2013;Gesualdo et al, 2013;Heaivilin, Gerbert, Page, & Gibbs, 2011;Jain & Kumar, 2015;Nagel et al, 2013;Signorini, Segre, & Polgreen, 2011;Velardi, Stilo, Tozzi, & Gesualdo, 2014). It was rare in these studies to compare Twitter data to other sources of surveillance data so the validity of inferences based on Twitter data was not clear.…”