Moving beyond benchmarks and competitions: towards addressing social media challenges in an educational context

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• dc.contributor.author Ognibene, Dimitri
• dc.contributor.author Donabauer, Gregor
• dc.contributor.author Theophilou, Emily
• dc.contributor.author Buršić, Sathya
• dc.contributor.author Lomonaco, Francesco
• dc.contributor.author Wilkens, Rodrigo
• dc.contributor.author Hernández Leo, Davinia
• dc.contributor.author Kruschwitz, Udo
• dc.date.accessioned 2023-06-07T06:17:27Z
• dc.date.available 2023-06-07T06:17:27Z
• dc.date.issued 2023
• dc.description.abstract Natural language processing and other areas of artificial intelligence have seen staggering progress in recent years, yet much of this is reported with reference to somewhat limited benchmark datasets. We see the deployment of these techniques in realistic use cases as the next step in this development. In particular, much progress is still needed in educational settings, which can strongly improve users’ safety on social media. We present our efforts to develop multi-modal machine learning algorithms to be integrated into a social media companion aimed at supporting and educating users in dealing with fake news and other social media threats. Inside the companion environment, such algorithms can automatically assess and enable users to contextualize different aspects of their social media experience. They can estimate and display different characteristics of content in supported users’ feeds, such as ‘fakeness’ and ‘sentiment’, and suggest related alternatives to enrich users’ perspectives. In addition, they can evaluate the opinions, attitudes, and neighbourhoods of the users and of those appearing in their feeds. The aim of the latter process is to raise users’ awareness and resilience to filter bubbles and echo chambers, which are almost unnoticeable and rarely understood phenomena that may affect users’ information intake unconsciously and are unexpectedly widespread. The social media environment is rapidly changing and complex. While our algorithms show state-of-the-art performance, they rely on task-specific datasets, and their reliability may decrease over time and be limited against novel threats. The negative impact of these limits may be exasperated by users’ over-reliance on algorithmic tools. Therefore, companion algorithms and educational activities are meant to increase users’ awareness of social media threats while exposing the limits of such algorithms. This will also provide an educational example of the limits affecting the machine-learning components of social media platforms. We aim to devise, implement and test the impact of the companion and connected educational activities in acquiring and supporting conscientious and autonomous social media usage.
• dc.description.sponsorship This work was mainly supported by the project COURAGE: A Social Media Companion Safeguarding and Educating Students funded by the Volkswagen Foundation, grant number 95563, 95564, 95566, 9B145. This work has also been partially funded by the National Research Agency of the Spanish Ministry (PID2020-112584RB-C33/MICIN/AEI/10.13039/501100011033, MDM-2015-0502). D. Hernández-Leo (Serra Húnter) acknowledges the support by ICREA under the ICREA Academia programme.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ognibene D, Donabauer G, Theophilou E, Buršić S, Lomonaco F, Wilkens R, Hernández-Leo D, Kruschwitz U. Moving beyond benchmarks and competitions: towards addressing social media challenges in an educational context. Datenbank Spektrum. 2023;23(1):27-39. DOI: 10.1007/s13222-023-00436-3
• dc.identifier.doi http://dx.doi.org/10.1007/s13222-023-00436-3
• dc.identifier.issn 1618-2162
• dc.identifier.uri http://hdl.handle.net/10230/57088
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Datenbank-Spektrum. 2023;23(1):27-39.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-112584RB-C33
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502
• dc.rights © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Social Media
• dc.subject.keyword Fake News
• dc.subject.keyword Hate Speech
• dc.subject.keyword Toxic Content
• dc.subject.keyword Education
• dc.subject.keyword Companion
• dc.title Moving beyond benchmarks and competitions: towards addressing social media challenges in an educational context
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion