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Peer-Review Record

A Scalable and Automated Framework for Tracking the Likely Adoption of Emerging Technologies

Information 2024, 15(4), 237; https://doi.org/10.3390/info15040237
by Lowri Williams *, Eirini Anthi and Pete Burnap
Reviewer 1: Anonymous
Reviewer 2:
Information 2024, 15(4), 237; https://doi.org/10.3390/info15040237
Submission received: 25 March 2024 / Revised: 17 April 2024 / Accepted: 18 April 2024 / Published: 19 April 2024
(This article belongs to the Section Information Processes)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I read the paper with interest. Even more: I read the paper several times. In my opinion the main drawback of the paper is as follows. Each chapter is nicely written and clear. At the same time the paper as a whole lacks few things. The most important drawback: the paper lacks clear orientation to the major audience. If the intended primary readers will be people with the social sciences background or people with the computer science background ? If the paper is oriented toward computer scientists the most important element of the paper is the data used for automatic sentiment classification. Since the algorithms used used are well known and used in many other studies the paper needs clearer explanation how the data used is different from other studies and why the results obtained are important in this case. If the primary audience are people with social sciences background then the paper likely needs better comparison with the qualitative methods used by the researchers in this community. So in my opinion it could be important and necessary to add some details into the paper that could make the focus of the paper oriented more precisely.

Another thing is that the Figure 4 is hard to read. I would suggest using graph showing the dominant sentiments in time. Likely several such graphs shown in parallel or one after another will be easier to read and understand.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

My biggest concern with the manuscript is that the workflow and he code that authors have written is not presented. It should be cited as a code repository or a Zenodo/Figshare/etc. dataset. The workflow should be rerunnable. IMHO this needs to be fixed before the manuscript is published. More details are in the attached review PDF. I consider this a major (even though technical) revision, do to significance of presenting the code.

Comments for author File: Comments.pdf

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I took author's comments and editions into account. Largely I can agree with them

Author Response

We have updated Figure 1 to include a more thorough explanation of the data work in the manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

Addition of the code and figure data makes a significant improvement of this publication. Actually, the XLS files for the figures contain even more information since now we have quantitative evaluation. Good.

The new Fig. 1 is handy, but it is hard to map it into the provided scripts; it wold be useful if authors note next each box on the Fig. 1 which script or command(s) perform the action specified in the box, what are its inputs and outputs (files, URLs, DB records, etc.). This would greatly help the reproducibility. I suggest doing this update before the publication.

Author Response

We have updated Figure 1 to include a more thoroughly explanation of the data work in the manuscript.

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