On July 3, 2025, in Colombo, LIRNEasia organized the “Day of Information Disorder” to disseminate research findings from two major studies: a nationally representative survey and an experimental study measuring the effectiveness of misinformation countering measures. The event brought together researchers, journalists, media professionals, tech innovators, and policy experts to address one of today’s most urgent challenges: information disorder. The day began with an introduction by Helani Galpaya, CEO of LIRNEasia, who set the tone by unpacking what information disorder is and why it matters. LIRNEasia researcher Shenali Bamaramannage followed with a thought-provoking presentation titled “Are we idiots?”, sharing key findings from LIRNEasia’s national research on the human factors influencing susceptibility to misinformation in Sri Lanka. 
On July 3, 2025, in Colombo, LIRNEasia organized the “Day of Information Disorder” to disseminate research findings from two major studies: a nationally representative survey and an experimental study measuring the effectiveness of misinformation countering measures. The event brought together researchers, journalists, media professionals, tech innovators, and policy experts to address one of today’s most urgent challenges: information disorder. The day began with an introduction by Helani Galpaya, CEO of LIRNEasia, who set the tone by unpacking what information disorder is and why it matters. LIRNEasia researcher Shenali Bamaramannage followed with a thought-provoking presentation titled “Are we idiots?”, sharing key findings from LIRNEasia’s national research on the human factors influencing susceptibility to misinformation in Sri Lanka. 
On the 1st of July 2025, LIRNEasia in collaboration with the University of Jaffna held an event titled Launch of the information disorder research in Sri Lanka and a forum on building digital resilience. The event centered around the launch of results from a LIRNEasia study assessing the ability of Tamil news readers in Sri Lanka to classify information as true/false, and measuring the effectiveness of popular countermeasures to misinformation, such as fact-checking and media literacy programs. The opening address was given by Prof. Sivakolundu Srisatkunarajah, Vice Chancellor of the University of Jaffna, talked about the digital revolution, the newer challenges arising due to the information disorder and the importance of information literacy as a counter measure. The chief guest at the event, the Hon. 
An Expert Round Table discussion on "Tackling online misinformation while protecting freedom of expression" held on the 11th of October 2021, as the second of a series of discussions under the theme of “Frontiers of Digital Economy”
LIRNEasia joined a webinar on Information Disorder organized by University of Cape Town on 6 May 2022. This event was based on the collaborative Global South report on Information Disorder where LIRNEasia authored the chapter on Asian region.
A white paper exploring the use of AI in classifying misinformation.
Over the past decade, both internet penetration and digital media user base have increased substantially.
We present a dataset consisting of 3468 documents in Bengali, drawn from Bangladeshi news websites and factchecking operations, annotated as CREDIBLE, FALSE, PARTIAL or UN-CERTAIN. The dataset has markers for the content of the document, the classification, the web domain from which each document was retrieved, and the date on which the document was published. We also present the results of misinformation classification models built for the Bengali language, as well as comparisons to prior work in English and Sinhala.
We present a dataset consisting of 3576 documents in Sinhala, drawn from Sri Lankan news websites and factchecking operations, annotated as CREDIBLE, FALSE, PARTIAL or UN- CERTAIN. The dataset has markers for the content of the document, the classification, the web domain from which each document was retrieved, and the date on which the document was published. We also present the results of misinformation classification models built for the Sinhala language, as well as comparisons to English benchmarks, and suggest that for smaller media ecosystems it may make more practical sense to model uncertainty instead of truth vs falsehood binaries.
As hate speech on social media becomes an ever-increasing problem, policymakers may look to more authoritarian measures for policing content. Several countries have already, at some stage, banned networks such as Facebook and Twitter (Liebelson, 2017).
This paper presents two colloquial Sinhala language corpora from the language efforts of the Data, Analysis and Policy team of LIRNEasia, as well as a list of algorithmically derived stopwords. The larger of the two corpora spans 2010 to 2020 and contains 28,825,820 to 29,549,672 words of multilingual text posted by 533 Sri Lankan Facebook pages, including politics, media, celebrities, and other categories; the smaller corpus amounts to 5,402,76 words of only Sinhala text extracted from the larger.
We summarize the state of progress in artificial intelligence as used for classifying misinforma- tion, or ’fake news’. Making a case for AI in an assistive capacity for factchecking, we briefly examine the history of the field, divide current work into ’classical machine learning’ and ’deep learning’, and for both, examine the work that has led to certain algorithms becoming the de facto standards for this type of text classification task.
In a practical experiment, we benchmark five common text classification algorithms - Naive Bayes, Logistic Regression, Support Vector Machines, Random Forests, and eXtreme Gradient Boosting - on multiple misinformation datasets, accounting for both data-rich and data-poor environments.
A whitepaper distilling LIRNEasia's current thoughts on the possibilities and issues with the computation extraction of syntactic and semantic language from digital text.