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Replication package for identify bot comments

Mehdi Golzadeh; Alexandre Decan; Eleni Constantinou; Tom Mens


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        <foaf:name>Tom Mens</foaf:name>
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    <dcat:keyword>GitHub, automated comments, distributed software development, classification model, empirical analysis</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-05-22</dct:issued>
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    <dct:description>&lt;p&gt;This repository contains the replication package for our study about identifying bots at the level of their activity in GitHub submitted to BotSE&amp;#39;21 conference (*&amp;quot;Identifying bot activity in GitHub pull request and issue comments&amp;quot;*).&lt;br&gt; A link to the paper will be added to this README as soon as the paper is accepted.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Ground-truth dataset&lt;/strong&gt;&lt;br&gt; The dataset is extracted from the ground-truth dataset of our study about [identifying bots](https://arxiv.org/abs/2010.03303) published in JSS journal.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Replication package&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;A- Dataset preparation.ipynb: This notebook splits the dataset to two disjoint set for training and test purposes. To avoid any conflict with GDPR regulations we&amp;#39;ve anonymised the account name columns.&lt;/p&gt; &lt;p&gt;B- Model construction.ipynb: We followed a Grid-search cross validation in this notebook to find the best classifier and construct the final mode. The replication package was originally created on Python 3.8&amp;nbsp; and the dependencies required to run these notebooks are listed in requirements.txt and can be automatically installed using pip install -r requirements.txt.&lt;/p&gt; &lt;p&gt;C- Model evaluation.ipynb: this notebook contains scripts to evaluate the classifier.&lt;/p&gt;</dct:description>
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