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While topic modelling tools help discover themes in documents, they employ a \u201cbag-of-words\u201d approach and require researchers to manually label categories, specify the number of topics a priori, and make assumptions about word distributions in documents. This paper proposes an alternative approach based on entity linking, which links word strings to entities from a knowledge base, to help solve issues associated with \u201cbag-of-words\u201d approaches by automatically identifying topics based on entity mentions. To study topic trends and popularity, we use four indicators\u2014Mann\u2013Kendall\u2019s test, Sen\u2019s slope analysis, <jats:italic>z<\/jats:italic>-score values and Kleinberg\u2019s burst detection algorithm. The combination of these indicators helps us understand which topics are particularly active (\u201chot\u201d topics), which are decreasing (\u201ccold\u201d topics or past \u201cbursty\u201d topics) and which are maturely developed. We apply the approach and indicators to the fields of Information Science and Accounting.<\/jats:p>","DOI":"10.1007\/s11192-019-03274-x","type":"journal-article","created":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T11:43:31Z","timestamp":1572608611000},"page":"357-379","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Application of entity linking to identify research fronts and trends"],"prefix":"10.1007","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3896-6049","authenticated-orcid":false,"given":"Mauricio","family":"Marrone","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,1]]},"reference":[{"issue":"3","key":"3274_CR1","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.acalib.2016.02.010","volume":"42","author":"C Adams","year":"2016","unstructured":"Adams, C., Buetow, S., Edlin, R., Zdravkovic, N., & Heyligers, J. 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