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Found 28 papers, 5 papers with code
Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities
Spurred by the recent rapid increase in the development and distribution of large language models (LLMs) across industry and academia, much recent work has drawn attention to safety- and security-related threats and vulnerabilities of LLMs, including in the context of potentially criminal activities.
Large Language Models in Cryptocurrency Securities Cases: Can a GPT Model Meaningfully Assist Lawyers?
Our research is the first to systematically study an LLM's legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct.
Susceptibility to Influence of Large Language Models
Data was collected from 1000 human participants using an online experiment, and 1000 simulated participants using engineered prompts and LLM completion.
The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic
Besides far-reaching public health consequences, the COVID-19 pandemic had a significant psychological impact on people around the world.
Testing Human Ability To Detect Deepfake Images of Human Faces
Participants' confidence in their answers was high and unrelated to accuracy.
Identifying Human Strategies for Generating Word-Level Adversarial Examples
Adversarial examples in NLP are receiving increasing research attention.
Explainable Verbal Deception Detection using Transformers
We then zoom in on vocabulary uniqueness and the correlation of LIWC categories with the outcome class (truthful vs deceptive).
Who is GPT-3? An Exploration of Personality, Values and Demographics
Our results show that GPT-3 scores similarly to human samples in terms of personality and - when provided with a model response memory - in terms of the values it holds.
Textwash -- automated open-source text anonymisation
The increased use of text data in social science research has benefited from easy-to-access data (e. g., Twitter).
Detecting DeFi Securities Violations from Token Smart Contract Code
Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains.
Confounds and Overestimations in Fake Review Detection: Experimentally Controlling for Product-Ownership and Data-Origin
In response, previous work has developed automated methods for the detection of deceptive product reviews. However, studies vary considerably in terms of classification performance, and many use data thatcontain potential confounds, which makes it difficult to determine their validity.
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification
Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e. g., the preservation of semantics and grammaticality).
A repeated-measures study on emotional responses after a year in the pandemic
The introduction of COVID-19 lockdown measures and an outlook on return to normality are demanding societal changes.
No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization
For sensitive text data to be shared among NLP researchers and practitioners, shared documents need to comply with data protection and privacy laws.
The Grievance Dictionary: Understanding Threatening Language Use
This paper introduces the Grievance Dictionary, a psycholinguistic dictionary which can be used to automatically understand language use in the context of grievance-fuelled violence threat assessment.
Too good to be true? Predicting author profiles from abusive language
The problem of online threats and abuse could potentially be mitigated with a computational approach, where sources of abuse are better understood or identified through author profiling.
Manipulating emotions for ground truth emotion analysis
Text data are being used as a lens through which human cognition can be studied at a large scale.
Women worry about family, men about the economy: Gender differences in emotional responses to COVID-19
Among the critical challenges around the COVID-19 pandemic is dealing with the potentially detrimental effects on people's mental health.
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples
Recent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood.
Violent music vs violence and music: Drill rap and violent crime in London
The current policy of removing drill music videos from social media platforms such as YouTube remains controversial because it risks conflating the co-occurrence of drill rap and violence with a causal chain of the two.
Measuring Emotions in the COVID-19 Real World Worry Dataset
This resulted in the Real World Worry Dataset of 5, 000 texts (2, 500 short + 2, 500 long texts).
How human judgment impairs automated deception detection performance
The hybrid-adjust condition did not deception detection performance.
Examining UK drill music through sentiment trajectory analysis
This paper presents how techniques from natural language processing can be used to examine the sentiment trajectories of gang-related drill music in the United Kingdom (UK).
Online influence, offline violence: Language Use on YouTube surrounding the 'Unite the Right' rally
We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally.
Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels
We found that the use of two sentiment patterns differed significantly depending on political leaning.
Identifying the sentiment styles of YouTube's vloggers
Vlogs provide a rich public source of data in a novel setting.
Automatic Detection of Fake News
The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content.
