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pro vyhledávání: '"Assenmacher, Dennis"'
We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diverse persona descriptions into LLM prompts across two studies, exploring
Externí odkaz:
http://arxiv.org/abs/2410.11745
There is an increase in the proliferation of online hate commensurate with the rise in the usage of social media. In response, there is also a significant advancement in the creation of automated tools aimed at identifying harmful text content using
Externí odkaz:
http://arxiv.org/abs/2406.04892
Autor:
Yu, Zehui, Sen, Indira, Assenmacher, Dennis, Samory, Mattia, Fröhling, Leon, Dahn, Christina, Nozza, Debora, Wagner, Claudia
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evide
Externí odkaz:
http://arxiv.org/abs/2405.08562
Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media. One notable consequence is the u
Externí odkaz:
http://arxiv.org/abs/2404.14244
Autor:
Sen, Indira, Assenmacher, Dennis, Samory, Mattia, Augenstein, Isabelle, van der Aalst, Wil, Wagner, Claudia
NLP models are used in a variety of critical social computing tasks, such as detecting sexist, racist, or otherwise hateful content. Therefore, it is imperative that these models are robust to spurious features. Past work has attempted to tackle such
Externí odkaz:
http://arxiv.org/abs/2311.01270
The characterization and detection of bots with their presumed ability to manipulate society on social media platforms have been subject to many research endeavors over the last decade. In the absence of ground truth data (i.e., accounts that are lab
Externí odkaz:
http://arxiv.org/abs/2302.00546
Autor:
Pfeffer, Juergen, Matter, Daniel, Jaidka, Kokil, Varol, Onur, Mashhadi, Afra, Lasser, Jana, Assenmacher, Dennis, Wu, Siqi, Yang, Diyi, Brantner, Cornelia, Romero, Daniel M., Otterbacher, Jahna, Schwemmer, Carsten, Joseph, Kenneth, Garcia, David, Morstatter, Fred
At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to t
Externí odkaz:
http://arxiv.org/abs/2301.11429
Autor:
Clever, Lena, Assenmacher, Dennis, Müller, Kilian, Seiler, Moritz Vinzent, Riehle, Dennis M., Preuss, Mike, Grimme, Christian
Nowadays fake news are heavily discussed in public and political debates. Even though the phenomenon of intended false information is rather old, misinformation reaches a new level with the rise of the internet and participatory platforms. Due to Fac
Externí odkaz:
http://arxiv.org/abs/2003.07595
Autor:
Valdez, André Calero, Adam, Lena, Assenmacher, Dennis, Burbach, Laura, Bonart, Malte, Frischlich, Lena, Schaer, Philipp
Publikováno v:
2019 IEEE International Professional Communication Conference (ProComm), Aachen, Germany, 2019, pp. 275-285
The digitization of the world has also led to a digitization of communication processes. Traditional research methods fall short in understanding communication in digital worlds as the scope has become too large in volume, variety, and velocity to be
Externí odkaz:
http://arxiv.org/abs/2001.00565
Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such (semi-)automated programs, it is still uncle
Externí odkaz:
http://arxiv.org/abs/1902.06691