Papers by Bennett Kleinberg
SOBR: A Corpus for Stylometry, Obfuscation, and Bias on Reddit (2024.lrec-main)
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| Challenge: | Existing corpora are limited in scope and can be used to collect data on author attributes. |
| Approach: | They propose to use subreddits, flairs, and self-reports as distant labels for author attributes (age, gender, nationality, personality, and political leaning) . |
| Outcome: | The proposed method could be used to infer author attributes from public posts despite their discreetness and anonymity . |
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification (2021.emnlp-main)
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| Challenge: | Recent work has raised the question of whether valid adversarial inputs are feasible. |
| Approach: | They analyze how human-generated adversarial examples compare to the best algorithms . they use crowdsourcing to modify words in an input text with immediate feedback . |
| Outcome: | The proposed algorithms are not more efficient than the best to generate natural-reading, sentiment-preserving examples. |
Identifying Human Strategies for Generating Word-Level Adversarial Examples (2022.findings-emnlp)
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| Challenge: | a recent study shows that word-level adversarial examples are more natural and grammatical correct than automated attacks. |
| Approach: | They analyze how humans generate word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammatical correctness. |
| Outcome: | The authors show that humans generate adversarial examples much more effortlessly than automated attacks. |
Identifying the sentiment styles of YouTube’s vloggers (D18-1)
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| Challenge: | Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog’s narrative time. |
| Approach: | They propose to automatically analyze the vlogs' linguistic styles using a dynamic intra-textual approach to sentiment analysis to shed light on the different temporal trajectories used by vloggers. |
| Outcome: | The proposed method predicts that vlogs with positive endings are the most prevalent in the sample. |
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples (2021.eacl-main)
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| Challenge: | Existing methods to detect adversarial examples are limited by the nature of these examples. |
| Approach: | They propose a frequency-guided word substitution algorithm that exploits adversarial word substitutions for the detection of adversarials. |
| Outcome: | The proposed algorithm outperforms existing detection methods by 13.0% on the SST-2 and IMDb sentiment datasets. |
Automatic Detection of Fake News (C18-1)
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| Challenge: | a growing number of fake news detection tools are needed to identify trustworthy news sources. |
| Approach: | They propose to use two novel datasets to automate the identification of fake news . they propose learning experiments to build accurate fake news detectors . |
| Outcome: | The proposed algorithms achieve accuracies of up to 76% and compare them with other tools . the proposed algorithms are based on satirical news sources and fact-checking websites . |