Papers by Neele Falk

14 papers
Moderation in the Wild: Investigating User-Driven Moderation in Online Discussions (2024.eacl-long)

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Challenge: Effective content moderation is imperative for fostering healthy and productive discussions in online domains.
Approach: They propose to document and release a dataset of comments in which users act as moderators.
Outcome: The proposed dataset contains 1000 comment-reply pairs with crowdsourced annotations from a large annotator pool and fine-grained annotation schema targeting the functions of moderation, stylistic properties(aggressiveness, subjectivity, sentiment), constructiveness, and individual perspectives of the annotators on the task.
Scaling up Discourse Quality Annotation for Political Science (2022.lrec-1)

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Challenge: Existing annotations on deliberative quality are time-consuming and suffer from class imbalance . ephd thesis: deliberation is not only the output of the decision making, but also the discussion that leads up to it.
Approach: They propose to use data augmentation techniques to improve deliberative quality predictions in a standard dataset.
Outcome: The proposed methods outperform classifiers based on linguistic features and argument quality annotations with or without data augmentation.
“Feels Feminine to Me”: Understanding Perceived Gendered Style through Human Annotations (2025.emnlp-main)

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Challenge: Using gender identity-based framing, language–gender associations are often grounded in the author’s gender identity, inferred from their language use.
Approach: They propose to operationalize the language–gender association as a perceived gender expression of language, focusing on how expression is externally interpreted by humans, independent of the author’s gender identity.
Outcome: The first dataset of itskind identifies 5,100 human annotations of perceived gendered style—human-written texts rated on a five-point scale from very feminine to very masculine.
Node Placement in Argument Maps: Modeling Unidirectional Relations in High & Low-Resource Scenarios (2023.acl-long)

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Challenge: Argument maps structure discourse into nodes with each node being an argument that supports or opposes its parent argument.
Approach: They propose a task of node placement: suggesting candidate nodes as parents for a new contribution.
Outcome: The proposed method improves the quality of the argument maps and reduces redundancy.
All That Glitters is Not Gold: A Gold Standard of Adjective-Noun Collocations for German (2020.lrec-1)

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Challenge: Using the GerCo dataset, we identify adjective-noun collocations in German and compare them with statistical associations measures.
Approach: They present a GerCo dataset of adjective-noun collocations for German, such as alter Freund ‘old friend’ and tiefe Liebe ‘deep love’.
Outcome: The GerCo dataset contains 4,732 positive and negative instances of collocations and covers all 16 semantic classes of adjectives defined in the German wordnet GermaNet.
StoryARG: a corpus of narratives and personal experiences in argumentative texts (2023.acl-long)

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Challenge: Narratives and argumentation are deeply related, according to psychologists and social scientists.
Approach: They annotated StoryARG from well-established corpora in computational argumentation and the Social Sciences, as well as comments to New York Times articles.
Outcome: The dataset contains 2451 textual spans annotated at two levels . it reveals positive impact on effectiveness for stories which illustrate a solution to a problem and in general, annotator-specific preferences .
Bridging Argument Quality and Deliberative Quality Annotations with Adapters (2023.findings-eacl)

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Challenge: Assessing the quality of an argument is a complex, highly subjective task . argument quality dimensions are complex and dependent on the context in which it is assessed .
Approach: They propose a multi-task learning framework that incorporates knowledge about related dimensions into the learning process.
Outcome: The proposed framework improves quality prediction in an extrinsic, out-of-domain task.
It Is Not Only the Negative that Deserves Attention! Understanding, Generation & Evaluation of (Positive) Moderation (2025.naacl-long)

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Challenge: Moderation is essential for maintaining and improving the quality of online discussions.
Approach: They annotate a dataset on 13 modes of discussion and use it to generate positive moderation.
Outcome: The proposed model shows that professional moderation generates higher ratings than professional moderated moderation, but prefers professional moderate in pairwise comparison.
Mining the uncertainty patterns of humans and models in the annotation of moral foundations and human values (2025.acl-long)

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Challenge: disagreement in annotation (HLV) is considered a constitutive feature of subjective tasks.
Approach: They investigate the relationship between disagreement in annotation and model uncertainty . they use linguistic features to calibrate models to HLV and uncertainty to analyze their impact on uncertainty.
Outcome: The proposed model uncertainty is calibrated to human label variation (HLV) the proposed model is calibrate to human labels, the authors show .
Annotator-Centric Active Learning for Subjective NLP Tasks (2024.emnlp-main)

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Challenge: Annotator-centric active learning addresses the high costs of collecting human annotations by strategically annotating the most informative samples.
Approach: They propose annotator-centric active learning which incorporates an annotation strategy following data sampling to approximate the full diversity of human judgments.
Outcome: The proposed approach improves data efficiency and performs well in annotator-centric evaluations.
Towards Argument Mining for Social Good: A Survey (2021.acl-long)

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Challenge: Argument Mining is a social science-based approach to analysis and analysis of arguments.
Approach: They propose a novel definition of argument quality which integrates the social science literature and the argument quality.
Outcome: The proposed definition of argument quality integrates the social science literature and the argument quality debate.
Reports of personal experiences and stories in argumentation: datasets and analysis (2022.acl-long)

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Challenge: Personal experiences and stories are important in argumentation, but they are not considered in the social sciences.
Approach: They propose to use annotated documents to scale-up the analysis using existing annotations.
Outcome: The proposed classifiers can identify documents containing personal experiences and reports . they can scale up to three domains and show that they perform well across domains.
Stories and Personal Experiences in the COVID-19 Discourse (2024.lrec-main)

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Challenge: 'storytelling' is a human strategy to use personal experiences to back-up one's position in debates about controversial topics.
Approach: They analyse the use of storytelling in the COVID-19 discourse by automatically annotating three publicly available Reddit datasets for a total of 367K comments.
Outcome: The proposed analysis on three publicly available Reddit datasets shows that storytelling is a powerful argumentative tool.
PerspectiveMod: A Perspectivist Resource for Deliberative Moderation (2025.emnlp-main)

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Challenge: Human moderators in online discussions face a heterogeneous range of tasks that go beyond content moderation, or policing.
Approach: They propose a dataset of online comments annotated for the question "Does this comment require moderation?" they aim to improve discussion quality by analyzing annotator perspectives and annotating their views.
Outcome: The proposed model is unique in its intentional variation across the level of moderation experience embedded in the source data, the annotator profiles and the individuality of the annnotator.

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