scholarly journals Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations (Preprint)

2018 ◽  
Author(s):  
Vasiliki Foufi ◽  
Tatsawan Timakum ◽  
Christophe Gaudet-Blavignac ◽  
Christian Lovis ◽  
Min Song
Keyword(s):  
Social Media ◽  
Chronic Diseases ◽  
Language Processing ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Privacy And Security ◽  
Mining System ◽  
Social Media Platforms ◽  
The Way

BACKGROUND Social media platforms constitute a rich data source for natural language processing tasks such as named entity recognition, relation extraction, and sentiment analysis. In particular, social media platforms about health provide a different insight into patient’s experiences with diseases and treatment than those found in the scientific literature. OBJECTIVE This paper aimed to report a study of entities related to chronic diseases and their relation in user-generated text posts. The major focus of our research is the study of biomedical entities found in health social media platforms and their relations and the way people suffering from chronic diseases express themselves. METHODS We collected a corpus of 17,624 text posts from disease-specific subreddits of the social news and discussion website Reddit. For entity and relation extraction from this corpus, we employed the PKDE4J tool developed by Song et al (2015). PKDE4J is a text mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. RESULTS Using PKDE4J, we extracted 2 types of entities and relations: biomedical entities and relations and subject-predicate-object entity relations. In total, 82,138 entities and 30,341 relation pairs were extracted from the Reddit dataset. The most highly mentioned entities were those related to oncological disease (2884 occurrences of cancer) and asthma (2180 occurrences). The relation pair anatomy-disease was the most frequent (5550 occurrences), the highest frequent entities in this pair being cancer and lymph. The manual validation of the extracted entities showed a very good performance of the system at the entity extraction task (3682/5151, 71.48% extracted entities were correctly labeled). CONCLUSIONS This study showed that people are eager to share their personal experience with chronic diseases on social media platforms despite possible privacy and security issues. The results reported in this paper are promising and demonstrate the need for more in-depth studies on the way patients with chronic diseases express themselves on social media platforms.

scholarly journals Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 354
Author(s):  
Tiberiu-Marian Georgescu
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Model Based ◽  
The Way

This paper describes the development and implementation of a natural language processing model based on machine learning which performs cognitive analysis for cybersecurity-related documents. A domain ontology was developed using a two-step approach: (1) the symmetry stage and (2) the machine adjustment. The first stage is based on the symmetry between the way humans represent a domain and the way machine learning solutions do. Therefore, the cybersecurity field was initially modeled based on the expertise of cybersecurity professionals. A dictionary of relevant entities was created; the entities were classified into 29 categories and later implemented as classes in a natural language processing model based on machine learning. After running successive performance tests, the ontology was remodeled from 29 to 18 classes. Using the ontology, a natural language processing model based on a supervised learning model was defined. We trained the model using sets of approximately 300,000 words. Remarkably, our model obtained an F1 score of 0.81 for named entity recognition and 0.58 for relation extraction, showing superior results compared to other similar models identified in the literature. Furthermore, in order to be easily used and tested, a web application that integrates our model as the core component was developed.

MOBILE SOCIAL MEDIA

2018 ◽  
Vol 8 (3) ◽  
pp. 8
Author(s):  
PHILIP ADEBO
Keyword(s):  
Social Media ◽  
Mobile Apps ◽  
Social Revolution ◽  
Social Media Platforms ◽  
The Way

The emergence of mobile connectivity is revolutionizing the way people live, work, interact, and socialize. Mobile social media is the heart of this social revolution. It is becoming a global phenomenon as it enables IP-connectivity for people on the move. Popular social media platforms such as Facebook, Twitter, and MySpace have made mobile apps for their users to have instant access from anywhere at any time. This paper provides a brief introduction into mobile social media, their benefits, and challenges.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

scholarly journals An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 79 ◽  
Author(s):  
Xiaoyu Han ◽  
Yue Zhang ◽  
Wenkai Zhang ◽  
Tinglei Huang
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Additional Information ◽  
Named Entity ◽  
Proposed Model ◽  
The Relationship ◽  
Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

scholarly journals Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256696
Author(s):  
Anna Keuchenius ◽  
Petter Törnberg ◽  
Justus Uitermark
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Language Processing ◽  
User Interaction ◽  
User Interactions ◽  
Online Interactions ◽  
Network Analyses ◽  
Social Media Platforms ◽  
Signed Network

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis.

scholarly journals The Use of Social Media in Public Relations at Non-Governmental Organisations in South Africa

2018 ◽  
Vol 10 (6(J)) ◽  
pp. 150-161
Author(s):  
G. Nchabeleng ◽  
CJ. Botha ◽  
CA Bisschoff
Keyword(s):  
South Africa ◽  
Social Media ◽  
Public Relations ◽  
Social Networking ◽  
Social Networking Sites ◽  
Instant Messaging ◽  
Public Relations Practitioners ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
The Way

Social media can be a useful tool in public relations in non-governmental organisations (NGOs), but do NGOs make use of social media in their quest for service delivery in South Africa? Social networking sites, blogging, email, instant messaging, and online journals are some of the technological changes that changed the way interaction between people and how they gather information. Although social media is mainly used for interactive dialogue and social interaction, the private sector soon realised that the web-based technologies (especially Facebook and Twitter) could also be a competitive business tool. Non-governmental organisations (NGOs) soon followed suit however at a slower pace than the general communication growth rate of social media in South Africa. This article examines if social networking sites have any impact on public relations practices of NGOs in South Africa – an environment where both customers and employees still struggle to take full advantage of social media. The critical literature findings increase the understanding of the current and future challenges of social media use in public relations at NGOs in South Africa. The study explores the main differences between traditional and social media, how social media is redefining public relations role, and shed some light on defining public relations practices, identify the uses, limitations and benefits of social media by public relations practitioners in NGOs. Recommendations for future communication research are given. Based on the literature, a qualitative research design collected data using semi-structured, individual interviews. The results revealed that social media platforms such as Facebook do have an effect, and even changed the way in which NGOs communicate. The study also revealed that social media certainly has an impact on public relations relationships. This means that it has become crucial that public relations practitioners at NOGs embrace and take advantage of social media, and that they should also invest in proper electronic platforms to reap the benefits of improved communication internally and externally.

Paradigm Shift for the Future

2021 ◽  
pp. 173-201
Keyword(s):  
Social Media ◽  
Case Studies ◽  
Paradigm Shift ◽  
Political Influence ◽  
Political Events ◽  
The Social ◽  
The World ◽  
The Future ◽  
Social Media Platforms ◽  
The Way

Through case studies of incidents around the world where the social media platforms have been used and abused for ulterior purposes, Chapter 6 highlights the lessons that can be learned. For good or for ill, the author elaborates on the way social media has been used as an arbiter to inflict various forms of political influence and how we may have become desensitized due to the popularity of the social media platforms themselves. A searching view is provided that there is now a propensity by foreign states to use social media to influence the user base of sovereign countries during key political events. This type of activity now justifies a paradigm shift in relation to our perception and utilization of computerized devices for the future.

scholarly journals Public Perception on Healthcare Services: Evidence from Social Media Platforms in China

2019 ◽  
Vol 16 (7) ◽  
pp. 1273 ◽  
Author(s):  
Guangyu Hu ◽  
Xueyan Han ◽  
Huixuan Zhou ◽  
Yuanli Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Public Perception ◽  
Content Volume ◽  
Healthcare Services ◽  
Service Improvement ◽  
Data Resource ◽  
Health Related Research ◽  
Health Related ◽  
Social Media Platforms

Social media has been used as data resource in a growing number of health-related research. The objectives of this study were to identify content volume and sentiment polarity of social media records relevant to healthcare services in China. A list of the key words of healthcare services were used to extract data from WeChat and Qzone, between June 2017 and September 2017. The data were put into a corpus, where content analyses were performed using Tencent natural language processing (NLP). The final corpus contained approximately 29 million records. Records on patient safety were the most frequently mentioned topic (approximately 8.73 million, 30.1% of the corpus), with the contents on humanistic care having received the least social media references (0.43 Million, 1.5%). Sentiment analyses showed 36.1%, 16.4%, and 47.4% of positive, neutral, and negative emotions, respectively. The doctor-patient relationship category had the highest proportion of negative contents (74.9%), followed by service efficiency (59.5%), and nursing service (53.0%). Neutral disposition was found to be the highest (30.4%) in the contents on appointment-booking services. This study added evidence to the magnitude and direction of public perceptions on healthcare services in China’s hospital and pointed to the possibility of monitoring healthcare service improvement, using readily available data in social media.

The Organised Self and Lifestyle Minimalism: Multimodal Deixis and Point of View in Decluttering Vlogs on YouTube

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Michele Zappavigna
Keyword(s):  
Social Media ◽  
Lived Experience ◽  
Point Of View ◽  
Visual Perspective ◽  
Semiotic Approach ◽  
Visual Frame ◽  
Social Media Platforms ◽  
The Way

AbstractThis paper explores how people present their relationship to their domestic objects in decluttering vlogs on YouTube, where they show the process of getting rid of undesired items. These videos are associated with discourses of ‘minimalism’ that are currently prevalent on social media platforms. The paper adopts a multimodal social semiotic approach, focusing on how language, gesture, and the visual frame coordinate intermodally to make meanings about objects. The multimodal construction of deixis in coordination with a type of ‘point-of-view shot’, filmed from the visual perspective of the vlogger, is examined. The broader aim is to investigate what these videos reveal about how digital semiotic capitalism is inflecting the lived experience of social media users. What is at stake is how people articulate intersubjective meanings about their experiences and relationships through the way they communicate about their objects.

scholarly journals An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde ◽  
Garima Sharma
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Gold Mine ◽  
Social Media Platforms ◽  
Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

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