Finding potential influences of a specific financial market in Twitter - Kanungsukkasem, Nont; Leelanupab, Teerapong (My University, 2015)
This paper proposes a new framework to identify and rank Twitter accounts of which short messages or tweets may influence a specific financial stock price. In this paper, we start by mainly focusing on the first step of our framework, selecting potential influencers based on their association with a particular stock market. With numerous limitations of Twitter service to access and acquire Twitters data, a new methodology is also proposed to use a List feature to select potential financial influencers. It requires only Lists of accounts that contain an official Twitter account of the specific financial market, provided by Twitter's users, through Twitter API. Our methodology is designed to use only a small amount of data, by which it can be practically used under the limitation of Twitter API's crawling rate. Experimental results show that most of the potential influencers returned by our methodology are similar and related to the specific financial markets, which are companies listed on S&P 500 in this experiment. Most of the returned influencers are the official accounts of company or organization from the same sector and news media with a special emphasis on the same industry. Comparing to Twitter's user recommendation service (Who-To-Follow) and a crowdsourcing search for topic experts (Cognos), our methodology returns more related accounts in both percentage and the number.