Purpose This paper aims to examine the state of the art in electronic records management (ERM) with the goal of identifying the prevailing research topics, gaps and issues in the field. Design/methodology/approach First, a wide search was performed on academic research databases, limited to the period between 2008–2018. Second, the search results were reviewed for relevance and duplicates. Finally, the study sources were checked against the list of journals and conferences ranked by computing research and education and JourQual. The final sample of 55 selected studies was analyzed in depth. Findings ERM has lost some research momentum due to being deeply embedded in affiliate information systems areas and the changing records management landscape. Additionally, the requirement models specified by Governmental/National Archives might have constrained technology innovation in ERM. A lack of application was identified for the social media research area. Research limitations/implications Limitations were encountered in available search tool functionality and keyword confusion leading to inflated search results. While effort has been made to obtain optimal search results, some relevant articles may have been omitted. Originality/value The last ERM state-of-the-art review was in 1997. A lot has changed since then. This paper will help researchers understand the current state of ERM research, its understudied areas and identify gaps for future studies.
Social media postings are now routinely used as proof of activities, events, or transactions in news media, academic institutions, governments, judicial courts, commerce, and various other organisations. The need to preserve social media content as records has drawn the interest of academic researchers, industry professionals, and policy makers. Despite the importance of this research area, selection of records from a pool of social media content remains an area of low research activity. This paper explores the use of Natural Language Processing methods to classify and select records from a pool of tweets (twitter social media content). We experiment with various characteristics of the data and NLP parameters with the goal of determining optimal parameters for training a supervised machine learning classifier. This paper can serve as an aid for understanding the fundamental elements of automating the selection of social media records.
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