The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users’ engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

Fake news on social media is a major challenge and studies have shown that fake news can propagate exponentially quickly in early stages. Therefore, we focus on early detection of fake news, and consider that only news article text is available at the time of detection, since additional information such as user responses and propagation patterns can be obtained only after the news spreads. However, we find historical user responses to previous articles are available and can be treated as soft semantic labels, that enrich the binary label of an article, by providing insights into why the article must be labeled as fake. We propose a novel Two-Level Convolutional Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection. We conduct experiments on one available dataset and a larger dataset collected by ourselves. Experimental results show that TCNN-URG outperforms the baselines based on prior approaches that detect fake news from article text alone.

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COVID-19 vaccine hesitancy has increased concerns about vaccine uptake required to overcome the pandemic and protect public health. A critical factor associated with anti-vaccine attitudes is the information shared on social media. In this work, we investigate misinformation communities and narratives that can contribute to COVID-19 vaccine hesitancy. During the pandemic, anti-science and political misinformation/conspiracies have been rampant on social media. Therefore, we investigate misinformation and conspiracy groups and their characteristic behaviours in Twitter data collected on COVID-19 vaccines. We identify if any suspicious coordinated efforts are present in promoting vaccine misinformation, and find two suspicious groups - one promoting a ‘Great Reset’ conspiracy which suggests that the pandemic is orchestrated by world leaders to take control of the economy, with vaccine related misinformation and strong anti-vaccine and anti-social messages such as no lock-downs; and another promoting the Bioweapon theory. Misinformation promoted is largely from the anti-vaccine and far-right communities in the 3-core of the retweet graph, with its tweets proportion of conspiracy and questionable sources to reliable sources being much higher. In comparison with the mainstream and health news, the right-leaning community is more influenced by the anti-vaccine and far-right communities, which is also reflected in the disparate vaccination rates in left and right U.S. states. The misinformation communities are also more vocal, either in vaccine or other discussions, relative to remaining communities, besides other behavioral differences. Furthermore, we investigate the COVID-19 vaccine narratives spread on social media. Besides misinformation narratives about vaccine safety, effectiveness and conspiracies, we find that rarer vaccine side-effects, reported less frequently in CDC VAERS reports, were more frequently discussed on social media, and in misinformation narratives, which also use other known tactics of science narratives distortion.

Background: Vaccine hesitancy, as defined by the WHO, is the reluctance or refusal to vaccinate despite the availability of vaccines and is one of the ten threats to global health in 2019. Vaccine hesitancy remains a complex matter influenced by multiple factors, especially in sub-Saharan Africa. Methods: We conducted a cross-sectional study between November 2021 and January 2022 among the general adult public seeking care at six different healthcare facilities in Kenya. The survey, in English, consisted of questions based on demographics, knowledge, and attitudes, including hesitancy towards the COVID-19 vaccine. Results: Of the 3996 surveys collected, 55.1% were from private, 19.5% from faith-based and 25.3% from government facilities., Approximately 81.0% of all the participants reported it was important to get a vaccine to protect other people from COVID-19, 79.9% reported they would take a vaccine to protect against COVID-19, yet 40.5% reported being hesitant to take the vaccine primarily due to side effects. Most of the variables were associated with receiving a vaccine. Only 52.1% of those seeking care from the government facility and 54.5% of those seeking care from the faith-based facility were vaccinated, compared to 81.5% seeking care from the private facilities (p < 0.001). More participants from private facilities felt that vaccines are safe as compared to those at the faith-based and government facilities (p < 0.001). Conclusion: Vaccine hesitancy in Kenya, even though much lower than reported in other countries, remains a dynamic problem. Mitigating strategies specific to Africa need to be developed to help address vaccine hesitancy in this part of the continent.

Hiccups, involuntary contraction of the diaphragm and intercostal muscle followed by an abrupt closure of the glottis, are a bothersome symptom that can be caused by a variety of illnesses or medications. Hiccups that persist for more than 48 hours should raise the suspicion of an underlying cause. Pneumonias, especially caused by the novel coronavirus, have rarely been reported to trigger hiccups. To the best of our knowledge, we present the first case in sub-Saharan Africa of a patient presenting to our institution with persistent hiccups and no other objective signs suggestive of underlying pneumonia. His high-resolution CT was suggestive of coronavirus disease 2019 (COVID-19) and a polymerase chain reaction (PCR) test confirmed the diagnosis. Our case highlights the need for a thorough history and physical examination in patients presenting with hiccups and the need to include COVID-19 in the differential diagnosis in such patients.

Kikuchi-Fujimoto disease (KFD) is a rare form of painful lymphadenopathy, usually cervical, which is more common in Southeast Asia and rarely reported from Africa. Symptoms are usually nonspecific (fever, night sweats, etc.), and can mimic more common diseases such as tuberculosis (TB) in endemic areas. We report a case of a 29-year-old black African woman who was admitted with headache, neck pain, fever, and lymphadenopathy. She was found to have aseptic meningitis, eventually attributed to TB based on cervical node biopsy, although further histology suggested KFD. Blood tests for systemic lupus erythematosus (SLE) were negative; she had already been commenced on anti-TB treatment and had responded well and so was continued with this therapy. She was also later diagnosed with Hashimoto's thyroiditis 3 months after her diagnosis of KFD. Five months after stopping TB treatment, she was readmitted with the same symptoms and associated painless lymphadenopathy. Repeat biopsy was morphologically similar to that of 2017, and repeat evaluation confirmed SLE. She has since been managed by a rheumatologist and continues to do well.