Who Wrote When? Author Diarization in Social Media Discussions (2024)

Abstract: We are proposing a novel framework for author diarization, i.e. attributing comments in online discussions to individual authors. We consider an innovative approach that merges pre-trained neural representations of writing style with author-conditional encoder-decoder diarization, enhanced by a Conditional Random Field with Viterbi decoding for alignment refinement. Additionally, we introduce two new large-scale German language datasets, one for authorship verification and the other for author diarization. We evaluate the performance of our diarization framework on these datasets, offering insights into the strengths and limitations of this approach.

## dataset construction we gathered two large-scale datasets comprising newspaper comments. these comments stem from forum discussions of articles that span from 2005 to 2021 on the german zeit-online forum discussion website foot_1 . all discussions are in german. for diarization, both a small and large dataset are provided. the small dataset is a subset of the large dataset and involves discussions with one to four participants, whereas the large dataset includes discussions with up to six participants. the small dataset helps in identifying potential issues early and allows for quicker iterations during model development. the large dataset, on the other hand, is essential for testing the model's performance in more realistic and extensive scenarios. in total, the large diarization dataset is composed of 440,822 discussions for training, 95,334 for development, and 94,398 for testing. the discussions are constrained to contain between one and ten comments each. longer discussions are randomly partitioned into shorter ones to adhere to this constraint. while the sets of authors in the verification and diarization datasets overlap, the datasets themselves are distinct, as they do not share the same comments. the comments are anonymized in that urls, author names, emails, and direct quotes in each dataset were automatically substituted with placeholders, which were then incorporated as new trainable tokens into the model. anonymization, or more accurately, text normalization, removes direct identifiers (e.g., names, emails) that could otherwise bias the model. this ensures that the model focuses on stylometric features that are indicative of an author's writing style, rather than any contentspecific identifiers. figure 2 illustrates the relationship between the percentage distribution of authors and their contribution to the total number of comments. the first bar chart shows the distribution of authors by the there is a clear disparity between the number of authors and their contribution to the total comments. the majority of authors are infrequent commenters, while a smaller fraction of highly active authors generate the majority of the content. this pattern can often be seen on social media and other content platforms, where a smaller number of users contribute most of the content. figure 3 depicts the distribution of the top ten topics discussed between 2016 and 2021. the topics were provided in the metadata during data collection. initially, topics were evenly distributed until 2020, when covid-19 articles surged and their discussions started dominating the discourse. further details and dataset assessment are provided in the appendix. ## method this section describes the different modules of our author diarization framework in detail. the incom- ## experimental setup the experimental setup discussion is divided into two parts, i.e., for authorship verification and for author diarization. ## results we conducted ten repetitions of model training. results are reported in ## conclusion digital text forensics is crucial for verifying the authenticity and integrity of electronic texts, aiding in investigations related to cybercrime, fraud, and misinformation. in conclusion, the diarization experiments underscore the significance of feature selection and show promising results for the proposed author-conditional encoder-decoder framework, enhanced by a conditional random field with viterbi decoding. these findings contribute to the ongoing efforts to enhance author diarization techniques. future directions of our work include end-to-end training of the proposed system.