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Introduction: This article introduces DisTrack, a methodology and a tool developed for tracking and analyzing misinformation within Online Social Networks (OSNs). DisTrack is designed to combat the spread of misinformation through a combination of Natural Language Processing (NLP) Social Network Analysis (SNA) and graph visualization. The primary goal is to detect misinformation, track its propagation, identify its sources, and assess the influence of various actors within the network. Methods: DisTrack's architecture incorporates a variety of methodologies including keyword search, semantic similarity assessments, and graph generation techniques. These methods collectively facilitate the monitoring of misinformation, the categorization of content based on alignment with known false claims, and the visualization of dissemination cascades through detailed graphs. The tool is tailored to capture and analyze the dynamic nature of misinformation spread in digital environments. Results: The effectiveness of DisTrack is demonstrated through three case studies focused on different themes: discredit/hate speech, anti-vaccine misinformation, and false narratives about the Russia-Ukraine conflict. These studies show DisTrack's capabilities in distinguishing posts that propagate falsehoods from those that counteract them, and tracing the evolution of misinformation from its inception. Conclusions: The research confirms that DisTrack is a valuable tool in the field of misinformation analysis. It effectively distinguishes between different types of misinformation and traces their development over time. By providing a comprehensive approach to understanding and combating misinformation in digital spaces, DisTrack proves to be an essential asset for researchers and practitioners working to mitigate the impact of false information in online social environments.

This study aims to demonstrate the methods for detecting negations in a sentence by uniquely evaluating the lexical structure of the text via word-sense disambiguation. The proposed framework examines all the unique features in the various expressions within a text to resolve the contextual usage of all tokens and decipher the effect of negation on sentiment analysis. The application of popular expression detectors skips this important step, thereby neglecting the root words caught in the web of negation and making text classification difficult for machine learning and sentiment analysis. This study adopts the Natural Language Processing (NLP) approach to discover and antonimize words that were negated for better accuracy in text classification using a knowledge base provided by an NLP library called WordHoard. Early results show that our initial analysis improved on traditional sentiment analysis, which sometimes neglects negations or assigns an inverse polarity score. The SentiWordNet analyzer was improved by 35%, the Vader analyzer by 20% and the TextBlob by 6%.

The outbreak of the SARS-CoV-2 novel coronavirus (COVID-19) has been accompanied by a large amount of misleading and false information about the virus, especially on social media. During the pandemic social media gained special interest as it went on to become an important medium of communication. This made the information being relayed on these platforms especially critical. In our work, we aim to explore the percentage of fake news being spread on Twitter as well as measure the sentiment of the public at the same time. We further study how the sentiment of fear is present among the public. In addition to that we compare the rate of spread of the virus per day with the rate of spread of fake news on Twitter. Our study is useful in establishing the role of Twitter, and social media, during a crisis, and more specifically during crisis management., Comment: 21 pages, 5 figures

Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidences about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking that can be used for either applications designed for the general public and by fact-checking organisations. FacTeR-Check enables retrieving fact-checked information, unchecked claims verification and tracking dangerous information over social media. This architectures involves several modules developed to evaluate semantic similarity, to calculate natural language inference and to retrieve information from Online Social Networks. The union of all these components builds a semi-automated fact-checking tool able of verifying new claims, to extract related evidence, and to track the evolution of a hoax on a OSN. While individual modules are validated on related benchmarks (mainly MSTS and SICK), the complete architecture is validated using a new dataset called NLI19-SP that is publicly released with COVID-19 related hoaxes and tweets from Spanish social media. Our results show state-of-the-art performance on the individual benchmarks, as well as producing a useful analysis of the evolution over time of 61 different hoaxes.

We present the first X-ray and UV/optical observations of a very bright and fast nova in the disc of M31, M31N 2013-01b. The nova reached a peak magnitude $R\sim$15 mag and decayed by 2 magnitudes in only 3 days, making it one of the brightest and fastest novae ever detected in Andromeda. From archival multi-band data we have been able to trace its fast evolution down to $U>21$ mag in less than two weeks and to uncover for the first time the Super-Soft X-ray phase, whose onset occurred 10-30 days from the optical maximum. The X-ray spectrum is consistent with a blackbody with a temperature of $\sim$50 eV and emitting radius of $\sim$4$\times 10^{9}$ cm, larger than a white dwarf radius, indicating an expanded region. Its peak X-ray luminosity, 3.5$\times 10^{37}$ erg s$^{-1}$, locates M31N 2013-01b among the most luminous novae in M31. We also unambiguously detect a short 1.28$\pm$0.02 h X-ray periodicity that we ascribe to the binary orbital period, possibly due to partial eclipses. This makes M31N 2013-01b the first nova in M31 with an orbital period determined. The short period also makes this nova one of the few known below the 2-3 h orbital period gap. All the observed characteristics strongly indicate that M31N 2013-01b harbours a massive white dwarf and a very low-mass companion, consistent with being a nova belonging to the disc population of the Andromeda Galaxy., Comment: 9 pages, 3 figures, 2 tables; accepted by the Astrophysical Journal