--- _id: '48232' author: - first_name: Milad full_name: Mirbabaie, Milad id: '88691' last_name: Mirbabaie - first_name: Jonas full_name: Rieskamp, Jonas id: '77643' last_name: Rieskamp - first_name: Lennart full_name: Hofeditz, Lennart last_name: Hofeditz - first_name: Stefan full_name: Stieglitz, Stefan last_name: Stieglitz citation: ama: 'Mirbabaie M, Rieskamp J, Hofeditz L, Stieglitz S. Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing. In: ; 2023.' apa: 'Mirbabaie, M., Rieskamp, J., Hofeditz, L., & Stieglitz, S. (2023). Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing. Hawaii International Conference on System Sciences (HICSS).' bibtex: '@inproceedings{Mirbabaie_Rieskamp_Hofeditz_Stieglitz_2023, title={Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing}, author={Mirbabaie, Milad and Rieskamp, Jonas and Hofeditz, Lennart and Stieglitz, Stefan}, year={2023} }' chicago: 'Mirbabaie, Milad, Jonas Rieskamp, Lennart Hofeditz, and Stefan Stieglitz. “Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing,” 2023.' ieee: 'M. Mirbabaie, J. Rieskamp, L. Hofeditz, and S. Stieglitz, “Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing,” presented at the Hawaii International Conference on System Sciences (HICSS), 2023.' mla: 'Mirbabaie, Milad, et al. Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing. 2023.' short: 'M. Mirbabaie, J. Rieskamp, L. Hofeditz, S. Stieglitz, in: 2023.' conference: name: Hawaii International Conference on System Sciences (HICSS) date_created: 2023-10-17T15:51:01Z date_updated: 2023-10-25T11:10:14Z language: - iso: eng status: public title: 'Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing' type: conference user_id: '77643' year: '2023' ... --- _id: '48468' author: - first_name: Jonas full_name: Rieskamp, Jonas id: '77643' last_name: Rieskamp - first_name: Milad full_name: Mirbabaie, Milad id: '88691' last_name: Mirbabaie - first_name: Marie full_name: Langer, Marie last_name: Langer - first_name: Alexander full_name: Kocur, Alexander last_name: Kocur citation: ama: 'Rieskamp J, Mirbabaie M, Langer M, Kocur A. From Virality to Veracity: Examining False Information on Telegram vs. Twitter. In: ; 2023.' apa: 'Rieskamp, J., Mirbabaie, M., Langer, M., & Kocur, A. (2023). From Virality to Veracity: Examining False Information on Telegram vs. Twitter. Hawaii International Conference.' bibtex: '@inproceedings{Rieskamp_Mirbabaie_Langer_Kocur_2023, title={From Virality to Veracity: Examining False Information on Telegram vs. Twitter}, author={Rieskamp, Jonas and Mirbabaie, Milad and Langer, Marie and Kocur, Alexander}, year={2023} }' chicago: 'Rieskamp, Jonas, Milad Mirbabaie, Marie Langer, and Alexander Kocur. “From Virality to Veracity: Examining False Information on Telegram vs. Twitter,” 2023.' ieee: 'J. Rieskamp, M. Mirbabaie, M. Langer, and A. Kocur, “From Virality to Veracity: Examining False Information on Telegram vs. Twitter,” presented at the Hawaii International Conference, 2023.' mla: 'Rieskamp, Jonas, et al. From Virality to Veracity: Examining False Information on Telegram vs. Twitter. 2023.' short: 'J. Rieskamp, M. Mirbabaie, M. Langer, A. Kocur, in: 2023.' conference: name: Hawaii International Conference date_created: 2023-10-25T11:12:06Z date_updated: 2023-10-25T11:15:12Z ddc: - '000' has_accepted_license: '1' language: - iso: eng status: public title: 'From Virality to Veracity: Examining False Information on Telegram vs. Twitter' type: conference user_id: '80546' year: '2023' ... --- _id: '36834' abstract: - lang: eng text: "AbstractIncreasing average temperatures and heat waves are having devasting impacts on human health and well-being but studies of heat impacts and how people adapt are rare and often confined to specific locations. In this study, we explore how analysis of conversations on social media can be used to understand how people feel about heat waves and how they respond. We collected global Twitter data over four months (from January to April 2022) using predefined hashtags about heat waves. Topic modelling identified five topics. The largest (one-third of all tweets) was related to sports events. The remaining two-thirds could be allocated to four topics connected to communication about climate-related heat or heat waves. Two of these were on the impacts of heat and heat waves (health impacts 20%; social impacts 16%), one was on extreme weather and climate change attribution (17%) and the last one was on perceptions and warning (13%). The number of tweets in each week corresponded well with major heat wave occurrences in Argentina, Australia, the USA and South Asia (India and Pakistan), indicating that people posting tweets were aware of the threat from heat and its impacts on the society. Among the words frequently used within the topic ‘Social impacts’ were ‘air-conditioning’ and ‘electricity’, suggesting links between coping strategies and financial pressure. Apart from analysing the content of tweets, new insights were also obtained from analysing how people engaged with Twitter tweets about heat or heat waves. We found that tweets posted early, and which were then shared by other influential Twitter users, were among the most popular. Finally, we found that the most popular tweets belonged to individual scientists or respected news outlets, with no evidence that misinformation about climate change-related heat is widespread.\r\n" author: - first_name: Kerstin K. full_name: Zander, Kerstin K. last_name: Zander - first_name: Jonas full_name: Rieskamp, Jonas id: '77643' last_name: Rieskamp - first_name: Milad full_name: Mirbabaie, Milad id: '88691' last_name: Mirbabaie - first_name: Mamoun full_name: Alazab, Mamoun last_name: Alazab - first_name: Duy full_name: Nguyen, Duy last_name: Nguyen citation: ama: 'Zander KK, Rieskamp J, Mirbabaie M, Alazab M, Nguyen D. Responses to heat waves: what can Twitter data tell us? Natural Hazards. Published online 2023. doi:10.1007/s11069-023-05824-2' apa: 'Zander, K. K., Rieskamp, J., Mirbabaie, M., Alazab, M., & Nguyen, D. (2023). Responses to heat waves: what can Twitter data tell us? Natural Hazards. https://doi.org/10.1007/s11069-023-05824-2' bibtex: '@article{Zander_Rieskamp_Mirbabaie_Alazab_Nguyen_2023, title={Responses to heat waves: what can Twitter data tell us?}, DOI={10.1007/s11069-023-05824-2}, journal={Natural Hazards}, publisher={Springer Science and Business Media LLC}, author={Zander, Kerstin K. and Rieskamp, Jonas and Mirbabaie, Milad and Alazab, Mamoun and Nguyen, Duy}, year={2023} }' chicago: 'Zander, Kerstin K., Jonas Rieskamp, Milad Mirbabaie, Mamoun Alazab, and Duy Nguyen. “Responses to Heat Waves: What Can Twitter Data Tell Us?” Natural Hazards, 2023. https://doi.org/10.1007/s11069-023-05824-2.' ieee: 'K. K. Zander, J. Rieskamp, M. Mirbabaie, M. Alazab, and D. Nguyen, “Responses to heat waves: what can Twitter data tell us?,” Natural Hazards, 2023, doi: 10.1007/s11069-023-05824-2.' mla: 'Zander, Kerstin K., et al. “Responses to Heat Waves: What Can Twitter Data Tell Us?” Natural Hazards, Springer Science and Business Media LLC, 2023, doi:10.1007/s11069-023-05824-2.' short: K.K. Zander, J. Rieskamp, M. Mirbabaie, M. Alazab, D. Nguyen, Natural Hazards (2023). date_created: 2023-01-14T10:51:36Z date_updated: 2023-01-14T10:52:35Z doi: 10.1007/s11069-023-05824-2 keyword: - Earth and Planetary Sciences (miscellaneous) - Atmospheric Science - Water Science and Technology language: - iso: eng publication: Natural Hazards publication_identifier: issn: - 0921-030X - 1573-0840 publication_status: published publisher: Springer Science and Business Media LLC status: public title: 'Responses to heat waves: what can Twitter data tell us?' type: journal_article user_id: '77643' year: '2023' ... --- _id: '33490' abstract: - lang: eng text: Algorithmic fairness in Information Systems (IS) is a concept that aims to mitigate systematic discrimination and bias in automated decision-making. However, previous research argued that different fairness criteria are often incompatible. In hiring, AI is used to assess and rank applicants according to their fit for vacant positions. However, various types of bias also exist for AI-based algorithms (e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment, we conducted a systematic literature review to identify suitable strategies for the context of hiring. We identified nine fundamental articles in this context and extracted four types of approaches to address unfairness in AI, namely pre-process, in-process, post-process, and feature selection. Based on our findings, we (a) derived a research agenda for future studies and (b) proposed strategies for practitioners who design and develop AIs for hiring purposes. author: - first_name: Jonas full_name: Rieskamp, Jonas id: '77643' last_name: Rieskamp - first_name: Lennart full_name: Hofeditz, Lennart last_name: Hofeditz - first_name: Milad full_name: Mirbabaie, Milad id: '88691' last_name: Mirbabaie - first_name: Stefan full_name: Stieglitz, Stefan last_name: Stieglitz citation: ama: 'Rieskamp J, Hofeditz L, Mirbabaie M, Stieglitz S. Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research. In: Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). ; 2023.' apa: Rieskamp, J., Hofeditz, L., Mirbabaie, M., & Stieglitz, S. (2023). Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research. Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). bibtex: '@inproceedings{Rieskamp_Hofeditz_Mirbabaie_Stieglitz_2023, title={Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research}, booktitle={Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS)}, author={Rieskamp, Jonas and Hofeditz, Lennart and Mirbabaie, Milad and Stieglitz, Stefan}, year={2023} }' chicago: Rieskamp, Jonas, Lennart Hofeditz, Milad Mirbabaie, and Stefan Stieglitz. “Approaches to Improve Fairness When Deploying AI-Based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research.” In Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023. ieee: J. Rieskamp, L. Hofeditz, M. Mirbabaie, and S. Stieglitz, “Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research,” presented at the Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023. mla: Rieskamp, Jonas, et al. “Approaches to Improve Fairness When Deploying AI-Based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research.” Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023. short: 'J. Rieskamp, L. Hofeditz, M. Mirbabaie, S. Stieglitz, in: Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023.' conference: end_date: 2023-01-06 name: Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS) start_date: 2023-01-03 date_created: 2022-09-27T12:39:12Z date_updated: 2023-02-06T14:39:51Z keyword: - fairness in AI - SLR - hiring - AI implementation - AI-based algorithms language: - iso: eng main_file_link: - url: https://hdl.handle.net/10125/102654 publication: Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS) status: public title: Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research type: conference user_id: '77643' year: '2023' ... --- _id: '34025' abstract: - lang: eng text: "Controversial topics like abortion or capital punishment inherently lack\r\nof correct answers or the right way to deal with. Thus, in order to find what is true,\r\nwhat is good, or what should be done, the involved parties need to debate. For the\r\npurpose of forming an opinion on a controversial topic someone needs to take in a\r\nlot of arguments on that topic to gather information which can be a time-consuming\r\nprocess. To increase efficiency, someone can use an argument search engine to quicken\r\nthe retrieval of relevant arguments. Although the usage of such a service reduces the\r\ntime to find arguments, there is still a lot of textual data that needs to be read. To this\r\nend, computational summarization approaches for arguments can limit the necessary\r\ntime for information review by generating short snippets capturing the main gist of\r\neach argument. Yet, we suggest that approaches that consider one argument at a\r\ntime show potential for further improvement in terms of efficiency during information\r\nreview. In fact, arguments on the same topic, like those retrieved by a search engine for\r\na certain query, partially cover the same content, e. g. arguments regarding the death\r\npenalty probably use deterrence as a point in favor of it. However, if the same aspect\r\nis central in multiple arguments, their snippets reflect this, which leads to redundancy\r\namong the snippets. Consequently, someone interested in gathering information on a\r\ncontroversial topic does not necessarily find new information in each snippet he or she\r\nreads.\r\nWe introduce the task of Contrastive Argument Summarization (CAS) which addresses\r\nthe aforementioned problem regarding existing argument summarization. An approach\r\nthat addresses CAS aims to produce contrastive snippets for each argument in a set\r\nof topic-related arguments. A contrastive snippet should represent the main gist of its\r\nargument, it should account for the argumentative nature of the text, and it should be\r\ndissimilar to the other topic-related arguments in order to reduce redundancy among\r\nthe snippets.\r\nWe propose two approaches addressing CAS, namely an extended version of the\r\nLexRank derivation by Alshomary et al. (2020), and an advancement of the work\r\nby Bista et al. (2020). Additionally, we develop two automatic measures to assess to\r\nwhich extent the snippets of one set are opposed. For evaluation, we compile a corpus\r\nusing the args.me search engine Wachsmuth et al. (2017b) to come close to the suggested area of application. Moreover, we conduct a manual annotation study to assess\r\napproaches’ effectiveness. We find that the graph-based approach is superior when it\r\ncomes to contrastiveness (i. e. snippets being dissimilar to topic-related arguments),\r\nand that the second approach outperforms the previous one and the unmodified version of Alshomary et al. (2020) when it comes to representativeness (i. e. snippets\r\ncapturing the main gist of an argument)." author: - first_name: Jonas full_name: Rieskamp, Jonas id: '77643' last_name: Rieskamp citation: ama: Rieskamp J. Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning.; 2022. apa: Rieskamp, J. (2022). Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning. bibtex: '@book{Rieskamp_2022, title={Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning}, author={Rieskamp, Jonas}, year={2022} }' chicago: Rieskamp, Jonas. Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning, 2022. ieee: J. Rieskamp, Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning. 2022. mla: Rieskamp, Jonas. Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning. 2022. short: J. Rieskamp, Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning, 2022. date_created: 2022-11-07T13:57:08Z date_updated: 2022-11-07T13:57:37Z language: - iso: eng main_file_link: - url: https://en.cs.uni-paderborn.de/fileadmin/informatik/fg/css/teaching/theses/thesis_final.pdf status: public supervisor: - first_name: Milad full_name: Alshomary, Milad id: '73059' last_name: Alshomary - first_name: Henning full_name: Wachsmuth, Henning id: '3900' last_name: Wachsmuth title: Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning type: mastersthesis user_id: '77643' year: '2022' ... --- _id: '33519' author: - first_name: Julian full_name: Marx, Julian last_name: Marx - first_name: Jonas full_name: Rieskamp, Jonas id: '77643' last_name: Rieskamp - first_name: Milad full_name: Mirbabaie, Milad id: '88691' last_name: Mirbabaie citation: ama: 'Marx J, Rieskamp J, Mirbabaie M. ‘Just a Normal Day in the Metaverse’ – Distraction Conflicts of Knowledge Work in Virtual Environments. In: Proceedings of the 33rd Australasian Conference on Information Systems. ; 2022.' apa: Marx, J., Rieskamp, J., & Mirbabaie, M. (2022). ‘Just a Normal Day in the Metaverse’ – Distraction Conflicts of Knowledge Work in Virtual Environments. Proceedings of the 33rd Australasian Conference on Information Systems. Proceedings of the 33rd Australasian Conference on Information Systems, Melbourne. bibtex: '@inproceedings{Marx_Rieskamp_Mirbabaie_2022, title={‘Just a Normal Day in the Metaverse’ – Distraction Conflicts of Knowledge Work in Virtual Environments}, booktitle={Proceedings of the 33rd Australasian Conference on Information Systems}, author={Marx, Julian and Rieskamp, Jonas and Mirbabaie, Milad}, year={2022} }' chicago: Marx, Julian, Jonas Rieskamp, and Milad Mirbabaie. “‘Just a Normal Day in the Metaverse’ – Distraction Conflicts of Knowledge Work in Virtual Environments.” In Proceedings of the 33rd Australasian Conference on Information Systems, 2022. ieee: J. Marx, J. Rieskamp, and M. Mirbabaie, “‘Just a Normal Day in the Metaverse’ – Distraction Conflicts of Knowledge Work in Virtual Environments,” presented at the Proceedings of the 33rd Australasian Conference on Information Systems, Melbourne, 2022. mla: Marx, Julian, et al. “‘Just a Normal Day in the Metaverse’ – Distraction Conflicts of Knowledge Work in Virtual Environments.” Proceedings of the 33rd Australasian Conference on Information Systems, 2022. short: 'J. Marx, J. Rieskamp, M. Mirbabaie, in: Proceedings of the 33rd Australasian Conference on Information Systems, 2022.' conference: end_date: 2022-12-07 location: Melbourne name: Proceedings of the 33rd Australasian Conference on Information Systems start_date: 2022-12-04 date_created: 2022-10-05T08:27:52Z date_updated: 2022-12-06T10:21:23Z language: - iso: eng main_file_link: - url: https://www.researchgate.net/publication/365926051_'Just_a_Normal_Day_in_the_Metaverse'_-_Distraction_Conflicts_of_Knowledge_Work_in_Virtual_Environments publication: Proceedings of the 33rd Australasian Conference on Information Systems status: public title: ‘Just a Normal Day in the Metaverse’ – Distraction Conflicts of Knowledge Work in Virtual Environments type: conference user_id: '77643' year: '2022' ... --- _id: '32247' author: - first_name: Milad full_name: Alshomary, Milad id: '73059' last_name: Alshomary - first_name: Jonas full_name: Rieskamp, Jonas id: '77643' last_name: Rieskamp - first_name: Henning full_name: Wachsmuth, Henning id: '3900' last_name: Wachsmuth citation: ama: 'Alshomary M, Rieskamp J, Wachsmuth H. Generating Contrastive Snippets for Argument Search. In: Proceedings of the 9th International Conference on Computational Models of Argument. ; 2022:21-31. doi:http://dx.doi.org/10.3233/FAIA220138' apa: Alshomary, M., Rieskamp, J., & Wachsmuth, H. (2022). Generating Contrastive Snippets for Argument Search. Proceedings of the 9th International Conference on Computational Models of Argument, 21–31. http://dx.doi.org/10.3233/FAIA220138 bibtex: '@inproceedings{Alshomary_Rieskamp_Wachsmuth_2022, title={Generating Contrastive Snippets for Argument Search}, DOI={http://dx.doi.org/10.3233/FAIA220138}, booktitle={Proceedings of the 9th International Conference on Computational Models of Argument}, author={Alshomary, Milad and Rieskamp, Jonas and Wachsmuth, Henning}, year={2022}, pages={21–31} }' chicago: Alshomary, Milad, Jonas Rieskamp, and Henning Wachsmuth. “Generating Contrastive Snippets for Argument Search.” In Proceedings of the 9th International Conference on Computational Models of Argument, 21–31, 2022. http://dx.doi.org/10.3233/FAIA220138. ieee: 'M. Alshomary, J. Rieskamp, and H. Wachsmuth, “Generating Contrastive Snippets for Argument Search,” in Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31, doi: http://dx.doi.org/10.3233/FAIA220138.' mla: Alshomary, Milad, et al. “Generating Contrastive Snippets for Argument Search.” Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31, doi:http://dx.doi.org/10.3233/FAIA220138. short: 'M. Alshomary, J. Rieskamp, H. Wachsmuth, in: Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31.' date_created: 2022-06-28T09:03:30Z date_updated: 2025-02-20T08:22:16Z department: - _id: '600' - _id: '660' doi: http://dx.doi.org/10.3233/FAIA220138 language: - iso: eng page: 21 - 31 project: - _id: '118' name: 'TRR 318 - INF: TRR 318 - Project Area INF' publication: Proceedings of the 9th International Conference on Computational Models of Argument status: public title: Generating Contrastive Snippets for Argument Search type: conference user_id: '3900' year: '2022' ...