Social media data mining: tools for collecting Twitter data

[ES] En la actualidad la minería de datos de los medios sociales suele estar centrada en recopilar información procedente de Twitter. Uno de los principales problemas en el análisis de redes sociales en estos casos es la adquisición de los datos en forma de grafo. La posibilidad de hacer esta labor «a mano», es impensable cuando se tratan redes de cientos o miles de nodos en constante comunicación entre ellos. Twitter, además, es un ejemplo muy claro de esta problemática. De cualquier trending topic se pueden generan cientos de tweets en una hora. Es necesario herramientas de adquisición de todos esos datos de una forma automatizada utilizando las posibilidades que la propia red social ofrece. Un ejemplo de herramienta que ofrece una obtención de datos, con limitaciones, es la herramienta Netlytic. No es la única, pero es sencilla de utilizar. En este documento se muestra un caso de uso de un proyecto recoplado con Netlytic y graficado mediante Gephi[EN] Currently, social media data mining is often focused on collecting information from Twitter. One of the main problems in social network analysis in these cases is the acquisition of data in the form of a graph. The possibility of doing this task "by hand" is unthinkable when dealing with networks of hundreds or thousands of nodes in constant communication with each other. Twitter, moreover, is a very clear example of this problem. Any trending topic can generate hundreds of tweets in an hour. Tools are needed to acquire all this data in an automated way using the possibilities offered by the social network itself. An example of a tool that offers data acquisition, with limitations, is the Netlytic tool. It is not the only one, but it is simple to use. This document shows a use case of a project collected with Netlytic and plotted using Gephi

The socialisation of the adolescent who carries out team sports: a transversal study of centrality with a social network analysis

[ES] Se analiza la actividad física realizada por los adolescentes del estudio, su relación con el sobrepeso (sobrepeso+obesidad) y la estructura de la red social de amistad establecida en adolescentes que practican deportes colectivos, utilizando diferentes parámetros indicativos de centralidad.[EN]Objectives To analyse the physical activity carried out by the adolescents in the study, its relationship to being overweight (overweight+obese) and to analyse the structure of the social network of friendship established in adolescents doing group sports, using different parameters indicative of centrality. Setting It was carried out in an educational environment, in 11 classrooms belonging to 5 Schools in Ponferrada (Spain). Participants 235 adolescents were included in the study (49.4% female), who were classified as normal weight or overweight. Primary and secondary outcome measures Physical Activity Questionnaire for Adolescents (PAQ-A) was used to study the level of physical activity. A social network analysis was carried out to analyse structural variables of centrality in different degrees of contact. Results 30.2% of the participants in our study were overweight. Relative to female participants in this study, males obtained significantly higher scores in the PAQ-A (OR: 2.11; 95% CI: 1.04 to 4.25; p value: 0.036) and were more likely to participate in group sport (OR: 4.59; 95% CI: 2.28 to 9.22; p value: 0.000). We found no significant relationship between physical activity and the weight status in the total sample, but among female participants, those with overweight status had higher odds of reporting high levels of physical exercise (OR: 4.50; 95% CI: 1.21 to 16.74; p value: 0.025). In terms of centrality, differentiating by gender, women who participated in group sports were more likely to be classified as having low values of centrality, while the opposite effect occurred for men, more likely to be classified as having high values of centrality. Conclusions Our findings, with limitations, underline the importance of two fundamental aspects to be taken into account in the design of future strategies: gender and the centrality within the social network depending on the intensity of contact they have with their peers

Feasibility of Social-Network-Based eHealth Intervention on the Improvement of Healthy Habits among Children

12 p.This study shows the feasibility of an eHealth solution for tackling eating habits and physical activity in the adolescent population. The participants were children from 11 to 15 years old. An intervention was carried out on 139 students in the intervention group and 91 students in the control group, in two schools during 14 weeks. The intervention group had access to the web through a user account and a password. They were able to create friendship relationships, post comments, give likes and interact with other users, as well as receive notifications and information about nutrition and physical activity on a daily basis and get (virtual) rewards for improving their habits. The control group did not have access to any of these features. The homogeneity of the samples in terms of gender, age, body mass index and initial health-related habits was demonstrated. Pre- and post-measurements were collected through self-reports on the application website. After applying multivariate analysis of variance, a significant alteration in the age-adjusted body mass index percentile was observed in the intervention group versus the control group, as well as in the PAQ-A score and the KIDMED score. It can be concluded that eHealth interventions can help to obtain healthy habits. More research is needed to examine the effectiveness in achieving adherence to these new habits.S

Prevalence of Comorbidities in Individuals Diagnosed and Undiagnosed with Alzheimer’s Disease in León, Spain and a Proposal for Contingency Procedures to Follow in the Case of Emergencies Involving People with Alzheimer’s Disease

pg. 1-15Alzheimer’s disease (AD) which is the most common type of dementia is characterized by mental or cognitive disorders. People su ering with this condition find it inherently di cult to communicate and describe symptoms. As a consequence, both detection and treatment of comorbidities associated with Alzheimer’s disease are substantially impaired. Equally, action protocols in the case of emergencies must be clearly formulated and stated. Methods: We performed a bibliography search followed by an observational and cross-sectional study involving a thorough review of medical records. A group of AD patients was compared with a control group. Each group consisted of 100 people and were all León residents aged 65 years. Results: The following comorbidities were found to be associated with AD: cataracts, urinary incontinence, osteoarthritis, hearing loss, osteoporosis, and personality disorders. The most frequent comorbidities in the control group were the following: eye strain, stroke, vertigo, as well as circulatory and respiratory disorders. Comorbidities with a similar incidence in both groups included type 2 diabetes mellitus, glaucoma, depression, obesity, arthritis, and anxiety. We also reviewed emergency procedures employed in the case of an emergency involving an AD patient. Conclusions: Some comorbidities were present in both the AD and control groups, while others were found in the AD group and not in the control group, and vice versa.S

Determining the severity of Parkinson’s disease in patients using a multi task neural network

[EN] Parkinson’s disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson’s severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson’s disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson’s Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson’s disease or non-severe Parkinson’s disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson’s outperforming the state-of-the-art proposals.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

A semantic social network analysis tool for sensitivity analysis and what-If scenario testing in alcohol consumption studies

15 páginasSocial Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network-generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error-prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called “ontology” that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.S

Heart disease risk prediction using deep learning techniques with feature augmentation

[EN] Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Men who have sex with men: An approach to social network analysis

[EN] Background: Dating apps for men who have sex with men (MSM) have favored unprotected sexual encounters; other unsafe practices, including drug use, are widespread. No evidence is available from the perspective of the structure of their relationships, a personal aspect included in all nursing meta-paradigms. Aim: To study the structure of MSM networks through dating and contact applications and this relationship to risky sexual activities such as condom use, chemsex (sex while using drug), and group sex. Design: Descriptive cross-sectional study. Sample: A total of 32 MSM participants from Madrid (Spain). Measurements: Socio-demographic and structural variables with Social Network Analysis (SNA) metrics. Data on condom use, drug use during encounters, and group sex were included. Results: Twenty-five percent of respondents practiced chemsex, and 75% of these used poppers. MSM with higher socioeconomic status participated in group sex sessions more frequently than those with lower socioeconomics. Within the network analysis, the relationships strong showed greater ease in having unprotected anal intercourse. Conclusion: SNA can be effective in the study of MSM sexual networks and their risk behaviors for community nurses to improve their interventions in sexual health promotionS

Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

Social networks have become information dissemination channels, where announcements are posted frequently; they also serve as frameworks for debates in various areas (e.g., scientific, political, and social). In particular, in the health area, social networks represent a channel to communicate and disseminate novel treatments' success; they also allow ordinary people to express their concerns about a disease or disorder. The Artificial Intelligence (AI) community has developed analytical methods to uncover and predict patterns from posts that enable it to explain news about a particular topic, e.g., mental disorders expressed as eating disorders or depression. Albeit potentially rich while expressing an idea or concern, posts are presented as short texts, preventing, thus, AI models from accurately encoding these posts' contextual knowledge. We propose a hybrid approach where knowledge encoded in community-maintained knowledge graphs (e.g., Wikidata) is combined with deep learning to categorize social media posts using existing classification models. The proposed approach resorts to state-of-the-art named entity recognizers and linkers (e.g., Falcon 2.0) to extract entities in short posts and link them to concepts in knowledge graphs. Then, knowledge graph embeddings (KGEs) are utilized to compute latent representations of the extracted entities, which result in vector representations of the posts that encode these entities' contextual knowledge extracted from the knowledge graphs. These KGEs are combined with contextualized word embeddings (e.g., BERT) to generate a context-based representation of the posts that empower prediction models. We apply our proposed approach in the health domain to detect whether a publication is related to an eating disorder (e.g., anorexia or bulimia) and uncover concepts within the discourse that could help healthcare providers diagnose this type of mental disorder. We evaluate our approach on a dataset of 2,000 tweets about eating disorders. Our experimental results suggest that combining contextual knowledge encoded in word embeddings with the one built from knowledge graphs increases the reliability of the predictive models. The ambition is that the proposed method can support health domain experts in discovering patterns that may forecast a mental disorder, enhancing early detection and more precise diagnosis towards personalized medicine

Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network

.In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS and TESS. The results obtained were promising, outperforming the state-of–the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations or financial brokers.S