Towards Ethical Content-Based Detection Of Online Influence Campaigns

Crothers, Evan; Japkowicz, Nathalie; Viktor, Herna L.

doi:10.1109/mlsp.2019.8918842

Cited by 6 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With LLM usage becoming more accessible by the day, there is rising concern regarding the dangers that users with ill intent may pose when they harness the power of LLMs. LLMs have been exploited to disseminate conspiracies, fake news, and propaganda [106]- [108], which pose a significant threat to society. This problem is further exacerbated by the ease of access that online channels such as X (formerly Twitter), Instagram, and Amazon Reviews offer.…”

Section: Llms In Spreading Misinformation and Disinformationmentioning

confidence: 99%

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Shah,

Thapa,

Acharya

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This paper explores the dual role of Large Language Models (LLMs) in the context of online misinformation and disinformation. In today's digital landscape, where the internet and social media facilitate the rapid dissemination of information, discerning between accurate content and falsified information presents a formidable challenge. Misinformation, often arising unintentionally, and disinformation, crafted deliberately, are at the forefront of this challenge. LLMs such as OpenAI's GPT-4, equipped with advanced language generation abilities, present a double-edged sword in this scenario. While they hold promise in combating misinformation by fact-checking and detecting LLM-generated text, their ability to generate realistic, contextually relevant text also poses risks for creating and propagating misinformation. Further, LLMs are plagued with many problems such as biases, knowledge cutoffs, and hallucinations, which may further perpetuate misinformation and disinformation. The paper outlines historical developments in misinformation detection and how it affects social media consumption, especially among youth, and introduces LLMs and their applications in various domains. It then critically analyzes the potential of LLMs to generate and counter misinformation and disinformation in sensitive topics such as healthcare, COVID-19, and political agendas. Further, it discusses mitigation strategies, ethical considerations, and regulatory measures, summarizing previous methods and proposing future research direction toward leveraging the benefits of LLMs while minimizing misuse risks. The paper concludes by acknowledging LLMs as powerful tools with significant implications in both spreading and combating misinformation in the digital age.

show abstract

Section: Llms In Spreading Misinformation and Disinformationmentioning

confidence: 99%

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Shah,

Thapa,

Acharya

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As with extremism and terrorism research, data access and data sharing in the disinformation research community is an ongoing struggle. Pushshift data has already been used in a number of papers on disinformation and social media trustworthiness (Crothers, Japkowicz, and Viktor 2019;Horne and Adali 2017;Zannettou et al 2019;Zhou et al 2019).…”

Section: Author Flair Textmentioning

confidence: 99%

The Pushshift Reddit Dataset

Baumgartner¹,

Zannettou

Keegan

et al. 2020

ICWSM

482

191

View full text Add to dashboard Cite

Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves.Reddit, the so called “front page of the Internet,” in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically.In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.

show abstract

Section: Author Flair Textmentioning

confidence: 99%

The Pushshift Reddit Dataset

Baumgartner¹,

Zannettou²,

Keegan³

et al. 2020

Preprint

View full text Add to dashboard Cite

Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves.Reddit, the so called "front page of the Internet," in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and hundreds of billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically.In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.

show abstract

Towards Ethical Content-Based Detection Of Online Influence Campaigns

Cited by 6 publications

References 17 publications

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

The Pushshift Reddit Dataset

The Pushshift Reddit Dataset

Contact Info

Product

Resources

About