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2 results for “Automatic moderation”
Conversational Networks For Automatic Online Moderation
<p><strong>Description. </strong>This repository contains several datasets of conversational networks, extracted from the chat messages exchanged by players of the <a href="https://www.spaceorigin.fr/">SpaceOrigin</a> MMORPG. Each graph represents a specific conversation, and belongs to one of two classes: <em>Abusive</em> (1) or <em>Non-abusive</em> (0). Vertices represent users, and edges represent the fact that the connected users exchanged message during the considered time period. Edges are weighted and directed: weights represent the intensity of the message exchanges, and directions represent who sent messages to whom.</p> <p>We provide two types of graphs: unsigned and signed. Unsigned graphs were extracted using the method described in paper [1], below. Version 1.0 of this dataset contain only a part of the conversations, subsampled to get balanced classes. Version 1.1 is extended to contain all available conversations, and there are much more <em>Non-abusive</em> than <em>Abusive</em> conversations. Signed graphs were extracted later, using the method described in publication [9] below. Each edge is described by an additional sign, that indicates the polarity of the messages exchanged by two users; friendly (positive) vs. hostile (negative). </p> <p>These datasets were used to train a classifier into automatically recognizing abusive messages. See the below papers for more details. The repository also contains some figures that appear in these papers.</p> <p><strong>Publications. </strong>The following papers used the unsigned version of the conversational networks. The extraction method is described in paper [1].</p> <ul> <li>[1] É. Papégnies, V. Labatut, R. Dufour & G. Linarès, “Conversational Networks for Automatic Online Moderation,” <em>IEEE Transactions on Computational Social Systems </em>6(1):38–55, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01999546">hal-01999546</a>⟩ DOI: <a href="https://doi.org/10.1109/TCSS.2018.2887240">10.1109/tcss.2018.2887240</a></li> <li>[2] É. Papegnies, R. Dufour, V. Labatut & G. Linarès. “Détection de messages abusifs au moyen de réseaux conversationnels,” in <em>8ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et informatiques</em> (MARAMI), 2017. ⟨<a href="https://hal.science/hal-01614279">hal-01614279</a>⟩</li> <li>[3] É. Papegnies, V. Labatut, R. Dufour, & G. Linares. “Graph-based Features for Automatic Online Abuse Detection,” in <em>International Conference on Statistical Language and Speech Processing</em> (SLSP), Springer, Lecture Notes in Computer Science 10583:70-81, 2017. ⟨<a href="https://hal.science/hal-01571639/">hal-01571639</a>⟩ DOI: <a href="http://doi.org/10.1007/978-3-319-68456-7_6">10.1007/978-3-319-68456-7_6</a></li> <li>[4] N. Cécillon. “Exploration de descripteurs de plongements de graphes pour la détection de messages abusifs,” MSc Thesis, <em>Université d'Avignon, </em>2019. ⟨<a href="https://dumas.ccsd.cnrs.fr/dumas-04073337">dumas-04073337</a>⟩</li> <li>[5] N. Cécillon, V. Labatut, R. Dufour, and G. Linarès, “Abusive Language Detection in Online Conversations by Combining Content- and Graph-based Features,” in <em>International Workshop on Modeling and Mining Socia-Media Driven Complex Networks</em>, Frontiers in Big Data 2:8, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02130205">hal-02130205</a>⟩ DOI: <a href="http://doi.org/10.3389/fdata.2019.00008">10.3389/fdata.2019.00008</a></li> <li>[6] N. Cécillon, V. Labatut, R. Dufour, & G. Linarès. “Tuning Graph2vec with Node Labels for Abuse Detection in Online Conversations,” in <em>11ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et</em> informatiques (MARAMI), 2020. <a href="https://hal.science/hal-02993571" target="_blank" rel="noopener">⟨hal-02993571⟩</a> <a href="http://ceur-ws.org/Vol-2750/paper8.pdf">Official Page</a></li> <li>[7] N. Cécillon, V. Labatut, R. Dufour & G. Linarès. “Graph embeddings for Abusive Language Detection,” <em>Springer Nature Computer Science</em> 2:37,<em> </em>2021<em>. </em>⟨<a href="https://hal.science/hal-03042171/">hal-03042171</a>⟩ DOI: <a href="http://doi.org/10.1007/s42979-020-00413-7">10.1007/s42979-020-00413-7</a></li> <li>[8] N. Cécillon, R. Dufour & V. Labatut. “Approche multimodale par plongements de texte et de graphes pour la détection de messages abusifs,” <em>Traitement Automatique des Langues</em><em> </em>62:13-38, 2021. ⟨<a href="https://hal.science/hal-03527016">hal-03527016</a>⟩ <a href="https://aclanthology.org/2021.tal-2.2/">Official Page</a></li> </ul> <p>The following publications use the signed version of the graphs. The modified extraction method is described in publication [9].</p> <ul> <li>[9] N. Cécillon. “Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection,” PhD Thesis, <em>Université d'Avignon, </em>2024. ⟨<a href="https://theses.hal.science/tel-04441308">tel-04441308</a>⟩</li> </ul> <p><strong>Funding. </strong>Part of this work was funded by a grant from the <em>Provence-Alpes-Côte-d'Azur</em> region (PACA, France) and the <em>Nectar de Code</em> company.</p> <p><strong>Citation. </strong>If you use this dataset, please cite paper [1] for the unsigned networks:</p> <p><br><code>@Article{Papegnies2019,</code><br><code> author = {Papegnies, Étienne and Labatut, Vincent and Dufour, Richard and Linarès, Georges},</code><br><code> title = {Conversational Networks for Automatic Online Moderation},</code><br><code> journal = {IEEE Transactions on Computational Social Systems},</code><br><code> year = {2019},</code><br><code> volume = {6},</code><br><code> number = {1},</code><br><code> pages = {38-55},</code><br><code> doi = {10.1109/TCSS.2018.2887240},</code><br><code>}</code></p> <p>and [9] for the signed ones:</p> <p><br><code>@PhdThesis{Cecillon2024,</code><br><code> author = {Cécillon, Noé},</code><br><code> title = {Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection},</code><br><code> school = {Université d'Avignon},</code><br><code> year = {2024},</code><br><code> type = {PhD Thesis},</code><br><code> address = {Avignon, FR},</code><br><code> url = {https://theses.fr/2024AVIG0100},</code><br><code>}</code></p>
Action Planning Makes Physical Activity More Automatic, Only If it Is Autonomously Regulated: A Moderated Mediation Analysis
<p>Dataset used for analysis. </p>
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