Identifying Real or Fake Articles: Towards better Language Modeling

By Sameer Badaskar, Sachin Agarwal, Shilpa Arora. 2008

63 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

GLTR: Statistical Detection and Visualization of Generated Text (2019, background): “Similar work ﬁnds that texts differ in perplexity under a language model (Beresneva, 2016), frequency of rare bigrams (Grechnikov et al., 2009), and n-gram frequencies (Badaskar et al., 2008).”
The Social Impact of Natural Language Processing (2016, methodology): “…automatic inference of user attributes, a common and interesting NLP task, whose solution also holds promise for many useful applications, such as recommendation engines and fraud or deception detection (Badaskar et al., 2008; Fornaciari and Poesio, 2014; Ott et al., 2011; Banerjee et al., 2014).”
The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study (2016, background): “Indeed, a number of previous works have dealt with the detection of fake reviews (Jindal and Liu, 2007; Badaskar et al., 2008; Mackiewicz, 2008; Jindal et al., 2010; Ott et al., 2011; Fornaciari and Poesio, 2014).”
A Stylometric Inquiry into Hyperpartisan and Fake News (2017, background): “…et al., 2012 Kwon et al., 2013 Mocanu et al., 2015 Tambuscio et al., 2015 Wei et al., 2013 Chen et al., 2015 Conroy et al., 2015 Rubin et al., 2015 Badaskar et al., 2008 Afroz et al., 2012 Rubin et al., 2016 of this argument is that the reputation and reliability of almost any website can be put…”
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text (2023, methodology): “Another stream of work attempts to distinguish machine-generated from human-written texts based on statistical irregularities in the entropy (Lavergne et al., 2008), perplexity (Beresneva, 2016) or in the n -gram frequencies (Badaskar et al., 2008).”
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense (2023, methodology): “, 2008), perplexity (Beresneva, 2016), and n-gram frequencies (Grechnikov et al., 2009; Badaskar et al., 2008).”