Seminar "Reflections of Scientific Conduct in Altmetrics"

Seminar Details

Lecturer: Prof. Dr. Wolf-Tilo Balke

Supervising Staff: Muhammad Usman

Degree Programs: Master (Computer Science, Business Informatics, IST, Data Science)

Credits: 5

Examination Requirements: Attendance in all sessions, Presentation & Discussion

Weekly Meetings: Tuesdays, 15:00-16:30 in Room IZ251

Kick-Off: 15.10.2024

The Seminar

Our seminars always focus on presentation skills: instead of requiring a written paper, we ask you to develop a captivating and authentic presentation. To prepare you for this, we will spend the first few weeks exploring how to give a good and engaging presentation. Through analyzing various talks, we will discover together what makes a great presentation, and later work on your presentation techniques through practical exercises. Each seminar presentation will be delivered in front of the entire group and followed by an in-depth discussion. This seminar offers you one of the last opportunities before entering the professional world to receive honest feedback on your presentation style and performance. Take advantage of it! If you're interested in learning how to give a convincing presentation and are open to honest feedback, this seminar is the right place for you.

The Seminar Theme

In recent years, the sheer volume of information in various fields-whether in academia, on the Web, or in social media-has grown at an unprecedented rate. As a result, evaluating the quality and impact of information has become paramount. Altmetrics and Scientometrics provide essential tools for assessing the impact and reach of scholarly research. While Scientometrics focuses on traditional citation-based analysis, Altmetrics measures the broader impact of research through online mentions, social media shares, and other digital platforms. As competition within the academic and scientific community increases, so does the pressure to produce and publish. This can sometimes lead to a decrease in adherence to rigorous standards and an increase in disinformation, either intentional or unintentional. With the proliferation of social media, these problems can escalate rapidly, spreading misinformation and fostering conspiracies. In addition, the increase in scientific retractions highlights the need for careful review and validation of research. Altmetrics and Scientometrics assessments play a key role in this context by providing a systematic approach to assessing the reliability and significance of research. They help identify influential studies, uncover emerging trends, and ensure that valuable knowledge is recognized and disseminated appropriately. By promoting transparency and accountability, this enhances the credibility of scientific endeavors and supports informed decision-making within the scientific community and beyond.

Seminar Topics

1. Measuring Research Impact with Altmetrics
   • Erdt, M., Nagarajan, A., Sin, S. C. J., & Theng, Y. L. (2016). Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media. Scientometrics, 109, 1117- 1166. [https://doi.org/10.1007/s11192-016-2077-0].
   • Cheng, X., Wang, H., Tang, L., Jiang, W., Zhou, M., & Wang, G. (2024). Open peer review correlates with altmetrics but not with citations: Evidence from Nature Communications and PLoS One. Journal of Informetrics, 18(3), 101540. [https://doi.org/10.1016/j.joi.2024.101540].
2. Do Altmetrics Correlate with Retraction
   • ​​​​​​​​​​​​​​Amiri, M., Yaghtin, M., & Sotudeh, H. (2024). How do tweeters feel about scientific misinformation: an infoveillance sentiment analysis of tweets on retraction notices and retracted papers. Scientometrics,129(1),261-287. [https://doi.org/10.1007/s11192-023-04871-7].
   • Shema, H., Hahn, O., Mazarakis, A., & Peters, I. (2019). Retractions from altmetric and bibliometric perspectives. Information-Wissenschaft & Praxis, 70(2-3), 98-110. [https://doi.org/10.1515/iwp-2019-2006].
   • Yaqub, W., Kay, J., & Goldwater, M. (2024). Foundations for Enabling People to Recognise Misinformation in Social Media News based on Retracted Science. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1), 1-38. [https://doi.org/10.1145/3637335].
3. Impact of Social Media on Scientific Dissemination
   • ​​​​​​​Ouchi, A., Saberi, M. K., Ansari, N., Hashempour, L., & Isfandyari-Moghaddam, A. (2019). Do altmetrics correlate with citations? A study based on the 1,000 most-cited articles. Information Discovery and Delivery, 47(4), 192-202. [https://doi.org/10.1108/IDD-07-2019-0050].
   • Argüello-Gutiérrez, C., & Moreno-López, R. (2024). Attitudes and practices of educational researchers towards the use of social media to disseminate science. Journal of Information Science, 01655515241245958. [https://doi.org/10.1177/01655515241245].
4. Social Media and Scientific misinformation Dissemination
   • ​​​​​​​​​​​​​​Bar-Ilan, J., & Halevi, G. (2021). Retracted articles–the scientific version of fake news. The psychology of fake news: Accepting, sharing, and correcting misinformation, 47-70. [https://library.oapen.org/bitstream/handle/20.500.12657/46921/1/9781000179033.pdf#page=60].
   • Abhari, R., Vincent, N., Dambanemuya, H. K., Bodon, H., & Horvát, E. Á. (2022). Twitter engagement with retracted articles: Who, when, and how. arXiv preprint arXiv:2203.04228. [10.48550/arXiv.2203.04228].
   • Lim, D., Toriumi, F., & Yoshida, M. (2021, December). Do you trust experts on Twitter? Successful correction of COVID-19-related misinformation. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 518- 523). [https://doi.org/10.1145/3486622.3493982].
   • Peng, H., Romero, D. M., & Horvát, E. Á. (2022). Dynamics of cross-platform attention to retracted papers. Proceedings of the National Academy of Sciences, 119(25), e2119086119. [https://doi.org/10.1073/pnas.2119086119].
5. Scientometrics Analysis to Detect Research Misconduct
   • ​​​​​​​​​​​​​​McIntosh, L. D., & Vitale, C. H. (2024). Forensic Scientometrics--An emerging discipline to protect the scholarly record. arXiv preprint arXiv:2404.00478. [https://doi.org/10.48550/arXiv.2404.00478].
   • Chakraborty, J., Pradhan, D. K., & Nandi, S. (2022). Research misconduct and citation gaming: a critical review on characterization and recent trends of research manipulation. Data Management, Analytics and Innovation: Proceedings of ICDMAI 2021, Volume 2, 485-492. [https://doi.org/10.1007/978-981-16-2937-2_30].
6. Citation Intention Analysis
   • Te, S., Barhoumi, A., Lentschat, M., Bordignon, F., Labbé, C., & Portet, F. (2022, October). Citation Context Classification: Critical vs Non-critical. In Proceedings of the Third Workshop on Scholarly Document Processing (pp. 49-53). [https://aclanthology.org/2022.sdp-1.6/].
   • Lahiri, A., Sanyal, D. K., & Mukherjee, I. (2023, June). CitePrompt: using prompts to identify citation intent in scientific papers. In 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL) (pp. 51-55). IEEE. [DOI: 10.1109/JCDL57899.2023.00017].
7. Post Publication Peer-review
   • ​​​​​​​​​​​​​​Maddi, A., Monneau, E., Guaspare-Cartron, C., Gargiulo, F., & Dubois, M. (2024). Streetlight effect in PubPeer comments: are Open Access publications more scrutinized?. Scientometrics, 1-17. [https://doi.org/10.1007/s11192-024-05053-9].
   • Bordignon, F. (2020). Self-correction of science: A comparative study of negative citations and post-publication peer review. Scientometrics, 124(2), 1225-1239. [https://doi.org/10.1007/s11192-020-03536-z].
8. Quality Assessment in Peer Reviews
   • ​​​​​​​Muangkammuen, P., Fukumoto, F., Li, J., & Suzuki, Y. (2023, November). IntermediateTask Transfer Learning for Peer Review Score Prediction. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop (pp. 40-47). [https://aclanthology.org/2023.ijcnlp-srw.6].
   • Ghosal, T., Verma, R., Ekbal, A., & Bhattacharyya, P. (2019, July). DeepSentiPeer: Harnessing sentiment in review texts to recommend peer review decisions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1120-1130). [DOI:10.18653/v1/P19-1106].
   • Verma, R., Roychoudhury, R., & Ghosal, T. (2022, November). The lack of theory is painful: Modeling harshness in peer review comments. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 925-935). [https://aclanthology.org/2022.aacl-main.67/​​​​​​​].