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Computational Analysis of Chat Transcripts

Library Assessment Conference 2024 poster presentation

Chat Transactions by Month & Year

It is easy to see the impact that increased promotion and dedicated staff has had over time. Interestingly, while there was growth in 2020, particularly in the fall semester, 2021 brought the highest usage numbers. We believe that this is due to the continuing hybrid nature of classes with the ongoing COVID crisis and higher expectations from professors than during the peak of lockdown. Numbers dropped in 2022, as many/most students began attending classes in person again.

Daily/Hourly Distribution

2015

 

2023​​​​​​​

The daily/hourly distribution tables were pulled from Springshare. In the beginning, it is easy to see that there was minimal usage and that the usage pattern was intermittent. By 2023, with regular promotion of the service and its hours, we have seen a stabilization of traffic. 

Affiliation by Email Input

Users have never been requiredd to input their contact information in order to provide them the option of anonymity for sensitive research questions. Despite that, there is a clear indication in 2020 that more users are self-identifying when beginning their chat transaction. Further, there has been an increase in student and staff/faculty users self-identifying, particularly notable between 2020-2021 for students. There has been an overall decline in users who do not input an email. We suspect that is due to greater familiarity with the utility of chat transactions.

Word Frequency

This word frequency cloud was gathered using Counter from the fifty largest chat transcripts between 2019-2023. It is likely that there is a disproportionate focus on research in these chats, given their length. There is a high occurence of research-based terms including: article, journal, database, source, research, and search. Once this method is used on the entire corpus, we expect to see more service and citation questions (see Meert-Williston & Sandieson, 2019).