Power of crowdsourcing in Twitter to find similar/related users

Abstract

Identifying similar users in social media, e.g., Twitter, is very useful for a variety of applications, such as promoting social connections by user recommendation and defining the type of user accounts for target advertisement. Although many existing methods are available, especially that of Similar-To Framework of Twitter, they require a massive amount of data to process. This has become a major obstacle for non-commercial organizations and in particular academic researchers to study and analyze the similarity, due to the resource requirement to handle large datasets and the limitations of Twitter API to access Tweets per day. Accordingly, this paper proposes a new method that uses only a small amount of data and can be applied by not exceeding Twitter API limitations. Our method, called List Voting, uses only Lists feature that is provided by Twitter to determine accounts of similar users who are likely to produce similar contents. All the Lists are created by Twitter users. Thus, the List can be considered as a crowdsourcing. We also study the characteristics of the definition of crowdsourcing to confirm the consideration. Our experimental result shows that our method gets the benefit of the power of this crowdsourcing and can provide a list of users that are similar or related to a specified user.