Hybrid Approach for Detecting Propagandistic Community and Core Node on Social Networks

People share their views and daily life experiences on social networks and form a network structure. The information shared on social networks can be unreliable, and detecting such kinds of information may reduce mass panic. Propaganda is a kind of biased or unreliable information that can mislead or intend to promote a political cause. The disseminators involved in spreading such information create a sophisticated network structure. Detecting such communities can lead to a safe and reliable network for the users. In this paper, a Boundary-based Community Detection Approach (BCDA) has been proposed to identify the core nodes in a propagandistic community that detects propagandistic communities from social networks with the help of interior and boundary nodes. The approach consists of two phases, one is to detect the community, and the other is to detect the core member. The approach mines nodes from the boundary as well as from the interior of the community structure. The leader Ranker algorithm is used for mining candidate nodes within the boundary, and the Constraint coefficient is used for mining nodes within the boundary. A novel dataset is generated from Twitter. About six propagandistic communities are detected. The core members of the propagandistic community are a combination of a few nodes. The experiments are conducted on a newly collected Twitter dataset consisting of 16 attributes. From the experimental results, it is clear that the proposed model outperformed other related approaches, including Greedy Approach, Improved Community-based 316 Robust Influence Maximization (ICRIM), Community Based Influence Maximization Approach (CBIMA), etc. It was also observed from the experiments that most of the propagandistic information is being shared during trending events around the globe, for example, at times of the COVID-19 pandemic.

Detecting twitter hate speech in COVID-19 era using machine learning and ensemble learning techniques

The COVID-19 pandemic has impacted every nation, and social isolation is the major protective method for the coronavirus. People express themselves via Facebook and Twitter. People disseminate disinformation and hate speech on Twitter. This research seeks to detect hate speech using machine learning and ensemble learning techniques during COVID-19. Twitter data was extracted from using its API with the help of trending hashtags during the COVID-19 pandemic. Tweets were manually annotated into two categories based on different factors. Features are extracted using TF/IDF, Bag of Words and Tweet Length. The study found the Decision Tree classifier to be effective. Compared to other typical ML classifiers, it has 98% precision, 97% recall, 97% F1-Score, and 97% accuracy. The Stochastic Gradient Boosting classifier outperforms all others with 99 percent precision, 97 percent recall, 98 percent F1-Score, and 98.04 percent accuracy.

Risk of SARS-CoV-2 Breakthrough Infection in Vaccinated Cancer Patients: A Retrospective Cohort Study.

Although messenger RNA (mRNA) vaccines have established efficacy for prevention of severe SARS-CoV2 infection in the general population, their effectiveness in patients with malignancy, especially those on anti-neoplastic therapies, remains an area of open research. In order to better understand the risk of developing breakthrough SARS-CoV-2 infection and the outcomes associated with breakthrough infection for cancer patients, individual patient data from a curated outcomes database at the University of Kansas were retrospectively reviewed to determine the rate of breakthrough infection during an 8-month period encompassing the height of the delta variant surge. Although the rate of breakthrough infection in cancer patients after two doses of an mRNA vaccine remained low at 1.1%, hospitalization and death rates were 27 and 5%, respectively. Patients with hematologic malignancies, especially multiple myeloma, and those on anti-neoplastic therapy at the time of vaccination were found to be at higher risk for developing breakthrough infection.

Evaluation of the Durability of the Immune Humoral Response to COVID-19 Vaccines in Patients With Cancer Undergoing Treatment or Who Received a Stem Cell Transplant.

Importance: The durability of the antibody response to COVID-19 vaccines in patients with cancer undergoing treatment or who received a stem cell transplant is unknown and may be associated with infection outcomes. Objective: To evaluate anti-SARS-CoV-2 spike protein receptor binding domain (anti-RBD) and neutralizing antibody (nAb) responses to COVID-19 vaccines longitudinally over 6 months in patients with cancer undergoing treatment or who received a stem cell transplant (SCT). Design, Setting, and Participants: In this prospective, observational, longitudinal cross-sectional study of 453 patients with cancer undergoing treatment or who received an SCT at the University of Kansas Cancer Center in Kansas City, blood samples were obtained before 433 patients received a messenger RNA (mRNA) vaccine (BNT162b2 or mRNA-1273), after the first dose of the mRNA vaccine, and 1 month, 3 months, and 6 months after the second dose. Blood samples were also obtained 2, 4, and 7 months after 17 patients received the JNJ-78436735 vaccine. For patients receiving a third dose of an mRNA vaccine, blood samples were obtained 30 days after the third dose. Interventions: Blood samples and BNT162b2, mRNA-1273, or JNJ-78436735 vaccines. Main Outcomes and Measures: Geometric mean titers (GMTs) of the anti-RBD; the ratio of GMTs for analysis of demographic, disease, and treatment variables; the percentage of neutralization of anti-RBD antibodies; and the correlation between anti-RBD and nAb responses to the COVID-19 vaccines. Results: This study enrolled 453 patients (mean [SD] age, 60.4 [13,1] years; 253 [56%] were female). Of 450 patients, 273 (61%) received the BNT162b2 vaccine (Pfizer), 160 (36%) received the mRNA-1273 vaccine (Moderna), and 17 (4%) received the JNJ-7846735 vaccine (Johnson & Johnson). The GMTs of the anti-RBD for all patients were 1.70 (95% CI, 1.04-2.85) before vaccination, 18.65 (95% CI, 10.19-34.11) after the first dose, 470.38 (95% CI, 322.07-686.99) at 1 month after the second dose, 425.80 (95% CI, 322.24-562.64) at 3 months after the second dose, 447.23 (95% CI, 258.53-773.66) at 6 months after the second dose, and 9224.85 (95% CI, 2423.92-35107.55) after the third dose. The rate of threshold neutralization (≥30%) was observed in 203 of 252 patients (80%) 1 month after the second dose and in 135 of 166 patients (81%) 3 months after the second dose. Anti-RBD and nAb were highly correlated (Spearman correlation coefficient, 0.93 [0.92-0.94]; P < .001). Three months after the second dose, anti-RBD titers were lower in male vs female patients (ratio of GMTs, 0.52 [95% CI, 0.34-0.81]), patients older than 65 years vs patients 50 years or younger (ratio of GMTs, 0.38 [95% CI, 0.25-0.57]), and patients with hematologic malignant tumors vs solid tumors (ratio of GMTs, 0.40 [95% CI, 0.20-0.81]). Conclusions and Relevance: In this cross-sectional study, after 2 doses of an mRNA vaccine, anti-RBD titers peaked at 1 month and remained stable over the next 6 months. Patients older than 65 years of age, male patients, and patients with a hematologic malignant tumor had low antibody titers. Compared with the primary vaccine course, a 20-fold increase in titers from a third dose suggests a brisk B-cell anamnestic response in patients with cancer.

Identifying propaganda from online social networks during COVID-19 using machine learning techniques.

COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task.

Machine learning based approaches for detecting COVID-19 using clinical text data.

Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.