A Study on the Impact of Crowd-Sourced Rating on Tweets for the Credibility of Information Spreading

Social media has been used extensively for information spreading during disasters. The problem occurs nowadays is overloaded of information from social media that is spread by people and confused the other people. Current function in Twitter allows people to ‘favorite’ tweets, perform re-tweet instantly, or added their own opinion on the tweet by quote and re-tweet. It is allowed the user to report tweets, sensitive or harmful tweets, and non-interested tweet, but there are no features to identify the credibility and accuracy of those tweets. Therefore, we proposed a technical solution where a prototype was developed in a Twitter-like environment by adding crowdsource rating features. The purpose of this research is to investigate the influence of crowd-sourced rating on tweets for decision making for the credibility of information spreading. A pilot study was conducted to a small real sample group of 31 respondents to know the respondent’s feedback on the prototype. The pilot study shows that most of the respondents were agree with the capabilities of crowdsourcing rating feature in identifying the accuracy and credibility of that information. The prototype design and the questionnaires have to be modified again, but the rating features was still the same. This prototype then redistributed to 139 respondents, and the respondents need to answer the control scenario part, prototype part, and the questionnaires. Control scenario design was similar like a prototype but without adding the crowdsource rating features. The questionnaires have divided into four parts, which is demographic information, social media usage, evaluation of the prototype, and evaluation on the usability of the prototype. Overall, the result from the questionnaires answer was a positive result where most of the respondents were agreed that crowdsourced rating feature was useful and helped them to identify and determine the accuracy and credibility of the information and help to prevent from spreading the misinformation in social media.