A Comparison Study of Search Strategy on Collecting Twitter Data for Drug Adverse Reaction

Abstract

A changes in the effects of drugs can be occured because the patient consumes two (or more) drugs or consumes them with a specific food or beverage resulting an unexpected adverse reaction. The abundance of data in social media can become the based of ingredients that is very valuable in constructing drug interaction analysis knowledge for pharmaceutical experts to produce solution for this problem. The data in this study comes from social media Twitter via its standard search API and be collected using software tools “R” via its Twitter library. We observed two scenario strategies in collecting data in order to find the best strategy between them that can retrieved largest number of result. Scenario 1 was based on a combination of two or more keywords, while the Scenario 2 uses only single keyword search mechanism followed by a filtering process. A list of drugs and food interaction related keyword was first declared before the process. There are 20 keywords to be tested, divided into 5 cases and categorized into 4 attributes for each case: Event (K), Drug (O), Effect (E) and Food Beverage (M). The experiment showed that Scenario 2 with single keyword and filtering strategy is the best keyword finding mechanism that collected as many as 1323 data, while the Scenario 1 that using keyword combination only collected 438 data.