Current researches on misinformation's causal

In an era where artificial intelligence (AI) is reshaping countless fields, the research community of social sciences needs to adapt to the changes posed by these technologies [1, 2]. In particular, data quality and authenticity play a significant role in social sciences [3], where the conclusions drawn rely heavily on data collected, for instance, from surveys. There are many traditional ways of gathering data, such as public datasets or private surveys, but AI has led to innovative approaches, like using agent-based models (ABMs). In recent years, the use of this paradigm has gained significant attention across a variety of fields, from economics and social sciences to artificial intelligence and computational biology [4, 5, 6]. ABMs allow researchers to simulate complex situations by modeling the behaviors and interactions of individual agents within a given environment [7]. These models provide a powerful way to understand emergent phenomena--such as market dynamics, social behaviors, or ecological systems--that arise from the independent actions and interactions of individual agents, each following its own set of rules. In spite of their flexibility, these models face some limitations, particularly when dealing with complex environments. One of the main challenges is that the agents' behaviors are programmed by the modeler based on assumptions or simplified rules. This rigid structure limits the ability to account for the full range of possible interactions that can emerge in real-world scenarios.

Fake news is a complex problem and can span text, images and video. For written articles in particular, there are several ways of generating fake news. A fake news article could be produced by selectively editing facts, including people's names, dates or statistics. An article could also be completely fabricated with made-up events or people. Fake news articles can also be machine-generated as advances in artificial intelligence make it particularly easy to generate misinformation.

Originally published on the Distant Whispers blog. From the coverage that ChatGPT, developed by OpenAI, has been receiving since its launch in November 2022, you would be forgiven for thinking that is the only technology story around. And it deserves the spotlight. Few had expected the jaw-dropping rapid strides that this technology has made in the last few years, and it will continue to wow us this year. It has opened a bottle, and a genie with unsurpassed powers has emerged.

OpenAI, which released the viral ChatGPT chatbot last year, unveiled a tool that's intended to help show if text has been authored by an artificial intelligence program and passed off as human. The tool will flag content written by OpenAI's products as well as other AI authoring software. However, the company said "it still has a number of limitations -- so it should be used as a complement to other methods of determining the source of text instead of being the primary decision-making tool." In the Microsoft Corp.-backed company's evaluations, only 26% of AI-written text was correctly identified. It also flagged 9% of human-written text as being composed by AI. The tool, called a classifier, will be available as a web app, along with some resources for teachers, the company said in a statement Tuesday.

Recent years have witnessed the rise of misinformation campaigns that spread specific narratives on social media to manipulate public opinions on different areas, such as politics and healthcare. Consequently, an effective and efficient automatic methodology to estimate the influence of the misinformation on user beliefs and activities is needed. However, existing works on misinformation impact estimation either rely on small-scale psychological experiments or can only discover the correlation between user behaviour and misinformation. To address these issues, in this paper, we build up a causal framework that model the causal effect of misinformation from the perspective of temporal point process. To adapt the large-scale data, we design an efficient yet precise way to estimate the Individual Treatment Effect (ITE) via neural temporal point process and gaussian mixture models. Extensive experiments on synthetic dataset verify the effectiveness and efficiency of our model. We further apply our model on a real-world dataset of social media posts and engagements about COVID-19 vaccines. The experimental results indicate that our model recognized identifiable causal effect of misinformation that hurts people's subjective emotions toward the vaccines.

Simon Fraser University's International CyberCrime Research Centre (ICCRC) is engaged in a new project to develop artificial intelligence tools to fight COVID-19-related misinformation campaigns on social media. Throughout the pandemic, anti-science theories on social media that portray COVID-19 as a hoax or downplay the risk of infection have contributed to unnecessary transmission and death. Some research suggests that one-in-three people have encountered false or misleading information about COVID-19 on social media. And while COVID-19 vaccines are rolling out, misinformation on social media still fuels vaccine hesitancy in Canada and resistance to public health measures such as mask wearing. To combat this, the ICCRC - in SFU's School of Criminology - has received federal funding from the Digital Citizenship Contribution Program for a six-month research project to develop an artificial intelligence tool to help social media platforms, online service providers and government agencies identify COVID-19 misinformation campaigns on social media and take appropriate action.

Over the 20 years to 2017, the network of actors spreading scientific misinformation about climate change has been increasingly integrated into US political philanthropy. That's according to a study that used natural language processing to analyse connections between the two fields. "The study introduces a new and broader pathway through which climate change misinformation travels, beyond the tendency of research to narrowly focus on the activities of think-tanks and fossil-fuel interests, often in isolation from mainstream American institutions like philanthropy," writes Justin Farrell of Yale University, US, in Environmental Research Letters (ERL). "Yet, as this study also shows, the impact of funding from fossil-fuel sources still plays an important role, revealing that the strength of the relationship between the misinformation network and philanthropy is strongest for people and organizations directly tied to such funding." Farrell employed novel machine learning capabilities to recognise and classify repeating themes and links in lists of attendees and speakers at philanthropic meetings, millions of words of written materials, and lists of board members and lifetime achievement award winners.