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Modeling opinion polarization on social media: Application to Covid-19 vaccination hesitancy in Italy
The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination. Thanks to a mean-field analysis we demonstrate that the resulting Fokker-Planck system permits to reproduce bimodal distributions of opinions as observed in polarization dynamics. The model is then applied to sentiment analysis data from social media platforms in Italy, in order to analyze the evolution of opinions about Covid-19 vaccination. We show through numerical simulations that the model is capable to describe correctly the formation of the bimodal opinion structure observed in the vaccine-hesitant dataset, which is witness of the known polarization effects that happen within closed online communities.
Journal Article
Automated data collection with R
A hands on guide to web scraping and text mining for both beginners and experienced users of R
* Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL.
* Provides basic techniques to query web documents and data sets (XPath and regular expressions).
* An extensive set of exercises are presented to guide the reader through each technique.
* Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management.
* Case studies are featured throughout along with examples for each technique presented.
* R code and solutions to exercises featured in the book are provided on a supporting website.
eBook
GloVe-CNN-BiLSTM Model for Sentiment Analysis on Text Reviews
Nowadays, social media networks generate a tremendous amount of social information from their users. To understand people’s views and sentimental tendencies on a commodity or an event timely, it is necessary to conduct text sentiment analysis on the views expressed by users. For the microblog comment data, it is always mixed with long and short texts, which is relatively complex. Especially for long text data, it contains a lot of content, and the correlation between words is more complex than that in short text. To study the sentiment classification of these mixed texts composed of long-text and short-text, this research proposes an optimized GloVe-CNN-BiLSTM-based sentiment analysis model. In this model, GloVe is used to vectorize words, and CNN is given to represent part space character. BiLSTM is used to build temporal relationship. Twitter’s comment data on COVID-19 is used as an experimental dataset. The results of the experiments suggest that this method can effectually identify the sentimental tendency of users’ online comments, and the accuracy of sentiment classification on complete-text, long-text, and short-text can achieve to 0.9565, 0.9509, and 0.9560, respectively, which is obviously higher than other deep learning models. At the same time, experiments show that this method has good field expansion.
Journal Article
SparkText: Biomedical Text Mining on Big Data Framework
Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment.
In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes.
This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.
Journal Article
A Wearable Silent Text Input System Using EMG and Piezoelectric Sensors
This paper introduces a wearable silent text input system designed to capture text input through silent speech, without generating audible sound. The system integrates Electromyography (EMG) and piezoelectric lead zirconate titanate (PZT) sensors in a miniaturized form that can be comfortably attached to the chin, making it both comfortable to wear and esthetically pleasing. The EMG sensor records muscle activity linked to specific tongue and jaw movements, while the PZT sensor measures the minute vibrations and pressure changes in the chin skin caused by silent speech. Data from both sensors are analyzed to capture the timing and intensity of the silent speech signals, allowing the extraction of key features in both time and frequency domain. Several machine learning (ML) models, including both feature-based and non-feature-based approaches commonly used for classification tasks, are employed and compared to detect and classify subtle variations in sensor signals associated with individual alphabet letters. To evaluate and compare the ML models, EMG and PZT signals for the eight most frequently used English letters are collected across one hundred trials each. Results showed that non-feature-based models, particularly the Fea-Shot Learning with fused EMG and PZT signals, achieved the highest accuracy (95.63%) and F1-score (95.62%). The proposed system’s accuracy and real-time performance make it promising for silent text input and assistive communication applications.
Journal Article
Recurrent concepts in data streams classification
This work addresses the problem of mining data streams generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repair the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learner can detect recurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models. The experimental evaluation on three text mining problems demonstrates the main advantages of the proposed system: it provides information about the recurrence of concepts and rapidly adapts decision models when drift occurs.
Journal Article
Topical analysis of migration coverage during lockdown in India by mainstream print media
Implementing countrywide lockdown measures in India, from March 2020 to May 2020 was a major step to deal with the COVID -19 pandemic crisis. The decision of country lockdown adversely affected the urban migrant population, and a large section of them was compelled to move out of the urban areas to their native places. The reverse migration garnered widespread media attention and coverage in electronic as well as print media. The present study focuses on the coverage of the issue by print media using descriptive natural language text mining. The study uses topic modelling, clustering, and sentiment analysis to examine the articles on migration issues during the lockdown period published in two leading English newspapers in India- The Times of India and The Hindu. The sentiment analysis results indicate that the majority of articles have neutral sentiment while very few articles show high negative or positive polarity. Descriptive topic modelling results show that transport, food security, special services, and employment with migration and migrants are the majorly covered topics after employing Bag of Words and TF-IDF models. Clustering is performed to group the article titles based on similar traits using agglomerative hierarchical clustering.
Journal Article
A novel medical image enhancement algorithm based on CLAHE and pelican optimization
Medical image enhancement is considered a challenging image-processing framework because the low quality of images resulting after acquisition and transmission seriously affects the clinical diagnosis and observation. In order to improve the medical image visual quality, a novel medical image enhancement algorithm that is based on contrast adaptive histogram equalization and pelican optimization algorithm is proposed in this work. The estimation process using our proposed model improves the efficiency of the operation and provides superior results in terms of image quality and contrast. There are three steps in the enhancement process. The primary step includes medical image generation using a Text-to-image generative model. Secondly, the estimation of the clip-limit, which controls the enhancing performance. Finally, the operation of enhancing the medical images using our proposed method. The simulation experiments prove that our proposed algorithm achieves superior performance qualitatively and quantitatively, compared with the state-of-the-art experimental methods, Upon a thorough examination and comparative analysis of performance parameters. Furthermore, the advantageous characteristic of this algorithm is its applicability in multiple types of images. Improving the quality of the medical images using our algorithm allows us to attain a superior visual impact on the processed image, and to increase the rate of conformity in the clinical diagnosis. Our proposed model illustrates the structure and forms of relevant details, contained in the medical images. This leads to an increase in overall contrast and enhances visual perception.
Journal Article
Developing Content for a mHealth Intervention to Promote Postpartum Retention in Prevention of Mother-To-Child HIV Transmission Programs and Early Infant Diagnosis of HIV: A Qualitative Study
Maternal attendance at postnatal clinic visits and timely diagnosis of infant HIV infection are important steps for prevention of mother-to-child transmission (PMTCT) of HIV. We aimed to use theory-informed methods to develop text messages targeted at facilitating these steps.
We conducted five focus group discussions with health workers and women attending antenatal, postnatal, and PMTCT clinics to explore aspects of women's engagement in postnatal HIV care and infant testing. Discussion topics were informed by constructs of the Health Belief Model (HBM) and prior empirical research. Qualitative data were coded and analyzed according to the construct of the HBM to which they related. Themes were extracted and used to draft intervention messages. We carried out two stages of further messaging development: messages were presented in a follow-up focus group in order to develop optimal phrasing in local languages. We then further refined the messages, pretested them in individual cognitive interviews with selected health workers, and finalized the messages for the intervention.
Findings indicated that brief, personalized, caring, polite, encouraging, and educational text messages would facilitate women bringing their children to clinic after delivery, suggesting that text messages may serve as an important \"cue to action.\" Participants emphasized that messages should not mention HIV due to fear of HIV testing and disclosure. Participants also noted that text messages could capitalize on women's motivation to attend clinic for childhood immunizations.
Applying a multi-stage content development approach to crafting text messages--informed by behavioral theory--resulted in message content that was consistent across different focus groups. This approach could help answer \"why\" and \"how\" text messaging may be a useful tool to support maternal and child health. We are evaluating the effect of these messages on improving postpartum PMTCT retention and infant HIV testing in a randomized trial.
Journal Article