A collaborative trend prediction method using the crowdsourced wisdom of web search engines

Abstract

PurposeThe purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.Design/methodology/approachIn this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.FindingsThe authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.Originality/valueThis paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.

The prediction of Bitcoin price through gold price using long short-term memory model

“…LSTM has shown its effectiveness in predicting the highly unpredictable price of Bitcoin [18]- [20]. However, previous studies on Bitcoin price prediction have mostly concentrated on improving LSTM models from a technical perspective [21], [22]. On the other hand, studies that have used non-LSTM machine learning techniques have incorporated economic or technical features, but their accuracy has been low [23]- [25].…”