Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process\u27s complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users\u27 interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making

DVD Versus Physiotherapist-Led Inhaler Education: A Randomised Controlled Trial

Correct technique with inhalers is vital for therapeutic effect. Efficacy of DVD inhaler instruction was investigated. Secondary aims were to examine feasibility of an inhaler technique outcome measure, and to compare knowledge and self-efficacy after DVD or individual education. This was a randomised controlled trial conducted in a regional hospital paediatric ward, involving new or existing paediatric inhaler users. Inhaler technique was assessed pre-education in existing inhaler users. Participants were then randomised to message equivalent education by DVD or individually with a physiotherapist. Inhaler technique, self-efficacy and knowledge were assessed immediately post- and three months after education. Twenty one participants received DVD or individual education. There were no significant differences between groups for technique, self-efficacy or knowledge at any time. The outcome measure was feasible for use in a research study. DVD education was equivalent to individual instruction to teach parents how to use inhalers with their child

Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process\u27s complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users\u27 interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making

Temporal behavioural analysis of extremists on social media: A machine learning based approach

Public opinion is of critical importance to businesses and governments. It represents the collective opinion and prevalent views about a certain topic, policy, or issue. Extreme public opinion consists of extreme views held by individuals that advocate and spread radical ideas for the purpose of radicalizing others. while the proliferation of social media gives unprecedented reach and visibility and a platform for freely expressing public opinion, social media fora can also be used for spreading extreme views, manipulating public opinions, and radicalizing others. In this work, we leverage data mining and analytics techniques to study extreme public opinion expressed using social medial. A dataset of 259, 904 tweets posted between 21/02/2016 and 01/05/2021 was collected in relation to extreme nationalism, hate speech, and supremacy. The collected data was analyzed using a variety to techniques, including sentiment analysis, named entity recognition, social circle analysis, and opinion leaders\u27 identification, and results related to an American politician and an American right-wing activist were presented. The results obtained are very promising and open the door to the ability to monitor the evolution of extreme views and public opinion online

Study of radical views on social media: Classification and group dynamics analysis

Social media platforms have changed the way extremist groups recruit, influence and potentially radicalize users online. Radicalization has drawn the attention of many researchers worldwide with its increasing presence on social media. Therefore, the ability to identify and classify radical views online and their potential role in radicalizing individuals is of critical importance. In this work, we address this challenge by combining a number of machine learning and natural language processing techniques such as sentiment analysis, named entity recognition, clustering, opinion leader identification, and social circle analysis to gain insights about radical views related to extreme nationalism, and understand the social dynamics underpinning extremists\u27 related interactions. The proposed approach was implemented and tested on more than 259, 000 tweets collected over a five-year span. The different analysis steps were presented and the results obtained were analyzed

Combining artificial intelligence and expert content analysis to explore radical views on twitter: Case study on far-right discourse

Public opinion is among the critical types of information that often inform policy changes and political strategies. Extreme public opinion is of particular importance due to the potential it has in leading to radical behaviors and violent actions. The monitoring and analysis of extreme public opinion are therefore of great interest to government officials seeking to maintain public order and preempt violent actions. Radicalization is the process employed to influence individuals to develop extreme views and behaviors. Due to their global reach and popularity, social media platforms are used tools for recruitment and radicalization. Recently, data mining and natural language processing techniques have been employed for the detection of extremism and radicalization online. However, the existing approaches focus on the classification of individuals as extremists or non-extremists, thus simplifying the concept of radicalization to a binary problem. Such approaches fail to capture the nuances and the complexity of the radicalization process that is influenced by many aspects such as peer pressure, social interactions, and the impact of prominent figures. Aiming at addressing those limitations, this work proposed a sophisticated approach for the analysis of extreme views expressed on social media. The proposed approach combines the power of artificial intelligence and natural language processing techniques with expert content analysis to achieve a fine-grained and detailed analysis of Twitter extremist content related to the Far-right ideology as a case study. A dataset of over 259,000 tweets collected over five years was used to test our approach, leading to sophisticated analytics and insights about Far-right extremism. The proposed approach can serve as a powerful decision support tool for governments for the analysis of extreme public opinion that is expressed online, and open the door for more effective and responsive decision making

Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: A systematic review and meta-analysis

Background Post-acute COVID-19 Syndrome is now recognized as a complex systemic disease that is associated with substantial morbidity. Objectives To estimate the prevalence of persistent symptoms and signs at least 12 weeks after acute COVID-19 at different follow-up periods. Data sources Searches were conducted up to October 2021 in Ovid Embase, Ovid Medline, and PubMed. Study eligibility criteria Articles in English that reported the prevalence of persistent symptoms among individuals with confirmed SARS-CoV-2 infection and included at least 50 patients with a follow-up of at least 12 weeks after acute illness. Methods Random-effect meta-analysis was performed to produce pooled prevalence for each symptom at 4 different follow-up time intervals. Between-studies heterogeneity was evaluated using the I2 statistic and was explored via meta-regression, considering several a priori study level variables. Risk of bias was assessed using the Joanna Briggs Institute (JBI) tool and the Newcastle-Ottawa Scale for prevalence studies and comparative studies, respectively. Results After screening 3209 studies, a total of 63 studies were eligible, with a total COVID-19 population of 257,348. The most commonly reported symptoms were fatigue, dyspnea, sleep disorder and concentration difficulty (32%, 25%, 24%, and 22% respectively at 3-12 months follow-up). There was substantial between-studies heterogeneity for all reported symptoms prevalence. Meta-regressions identified statistically significant effect modifiers: world region, male gender, diabetes mellitus, disease severity and overall study quality score. Five of six studies including a comparator group consisting of COVID-19 negative cases observed significant adjusted associations between COVID-19 and several long-term symptoms. Conclusions This systematic review found that a large proportion of patients experience PACS 3 to 12 months after recovery from the acute phase of COVD-19. However, available studies of PACS are highly heterogeneous. Future studies need to have appropriate comparator groups, standardized symptoms definitions and measurements and longer follow-up

Antimicrobial Protegrin-1 Forms Amyloid-Like Fibrils with Rapid Kinetics Suggesting a Functional Link

Protegrin-1 (PG-1) is an 18 residues long, cysteine-rich β-sheet antimicrobial peptide (AMP). PG-1 induces strong cytotoxic activities on cell membrane and acts as a potent antibiotic agent. Earlier we reported that its cytotoxicity is mediated by its channel-forming ability. In this study, we have examined the amyloidogenic fibril formation properties of PG-1 in comparison with a well-defined amyloid, the amyloid-β (Aβ1–42) peptide. We have used atomic force microscopy (AFM) and thioflavin-T staining to investigate the kinetics of PG-1 fibrils growth and molecular dynamics simulations to elucidate the underlying mechanism. AFM images of PG-1 on a highly hydrophilic surface (mica) show fibrils with morphological similarities to Aβ1–42 fibrils. Real-time AFM imaging of fibril growth suggests that PG-1 fibril growth follows a relatively fast kinetics compared to the Aβ1–42 fibrils. The AFM results are in close agreement with results from thioflavin-T staining data. Furthermore, the results indicate that PG-1 forms fibrils in solution. Significantly, in contrast, we do not detect fibrillar structures of PG-1 on an anionic lipid bilayer 2-dioleoyl-sn-glycero-3-phospho-L-serine/1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine; only small PG-1 oligomers can be observed. Molecular dynamics simulations are able to identify the presence of these small oligomers on the membrane bilayer. Thus, our current results show that cytotoxic AMP PG-1 is amyloidogenic and capable of forming fibrils. Overall, comparing β-rich AMPs and amyloids such as Aβ, in addition to cytotoxicity and amyloidogenicity, they share a common structural motif, and are channel forming. These combined properties support a functional relationship between amyloidogenic peptides and β-sheet-rich cytolytic AMPs, suggesting that amyloids channels may have an antimicrobial function