Effective Factors on Eating Disorders Prevention Methods; Analysis of Food-Related Data on Twitter

Aims: Eating disorders are making a point of challenge for health related researches. Using big data for this type of researches can effectively help researchers use a beneficial resource of information worldwide in realtime. This study aimed to introduce a more accurate index for analyzing food related data and making relations between peoplechr s opinions and the prevention treatments for eating disorders. Instrument Methods: In this data mining study, more than 2 million eating related tweets were collected from Twitter in 2017 and analyzed by novel methods for big data research. Three main indicators (Basic sentiment rate, Health rate, and Relation rate) were used to predict if every user is more likely to have a healthy or unhealthy diet. Finally, these parameters were normalized, clustered, and combined to obtain an overall sentiment rate. Findings: Location and gender were estimated as effective indicators making the relationship between peoplesch s opinion and prevention treatments for eating disorders. Some combinations of factors were also considered influencing indicators when applied together, such as gender+age and gender+location. Conclusion: Punishment/reward combination criteria that are predicted with both gender and location data by FSR index is the most effective factor in making the relationship between peopleschr s opinion and prevention treatments for eating disorders.