Intelligent System for Predicting Suicidal Behaviour from Social Media and Health Data

It is evident that there has been enormous growth in terrorist attacks in recent years. The idea of online terrorism has also been growing its roots in the internet world. These types of activities have been growing along with the growth in internet technology. These types of events include social media threats such as hate speeches and comments provoking terror on social media platforms such as twitter, Facebook, etc. These activities must be prevented before it makes an impact. In this paper, we will make various classifiers that will group and predict various terrorism activities using k-NN algorithm and random forest algorithm. The purpose of this project is to use Global Terrorism Database as a dataset to detect terrorism. We will be using GTD which stands for Global Terrorism Database which is a publicly available database which contains information on terrorist event far and wide from 1970 through 2017 to train a machine learning-based intelligent system to predict any future events that could bring threat to the society.

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Understanding Demographic Bias and Representation in Social Media Health Data

Companion Publication of the 10th ACM Conference on Web Science - WebSci '19 ◽

10.1145/3328413.3328415 ◽

2019 ◽

Cited By ~ 1

Author(s):

Nina Cesare ◽

Christan Grant ◽

Elaine O. Nsoesie

Keyword(s):

Social Media ◽

Health Data

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How weather influenced the mood of people during the COVID-19 lockdown in Catalonia: a review of Twitter posts

Advances in Science and Research ◽

10.5194/asr-18-1-2021 ◽

2021 ◽

Vol 18 ◽

pp. 1-5

Author(s):

Tomàs Molina ◽

Alex Sancliment ◽

Jofre Janué

Keyword(s):

Public Health ◽

Social Media ◽

Health Data ◽

Public Health Data ◽

Social Media Platforms ◽

News Programs

Abstract. This article is the result of a campaign done during the COVID-19 lockdown in Catalonia. The Television of Catalonia audience was involved in an action to inform about the weather from their own homes by posting Twitter videos. Some of the videos were shown on air in the weather segment of the television station's main news programs. We have correlated participation in the campaign with meteorological and public health data and found that weather is related to the mood of people when using social media platforms such as Twitter.

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Similarity approximation of Twitter Profiles

10.20944/preprints202106.0196.v1 ◽

2021 ◽

Author(s):

Niloufar Shoeibi ◽

Nastaran Shoeibi ◽

Pablo Chamoso ◽

Zakie Alizadehsani ◽

Juan M. Corchado

Keyword(s):

Social Media ◽

Language Processing ◽

User Satisfaction ◽

Intelligent System ◽

Media Analysis ◽

Related Information ◽

Social Media Platforms ◽

Dynamic Time ◽

Content Similarity ◽

Processing Techniques

Social media platforms are entirely an undeniable part of the lifestyle from the past decade. Analyzing the information being shared is a crucial step to understand humans behavior. Social media analysis is aiming to guarantee a better experience for the user and risen user satisfaction. But first, it is necessary to know how and from which aspects to compare users with each other. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official Twitter API. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for comparison of the behavioral ratios of two profiles. Next, Graph Network Analysis is used for monitoring the interactions of the user and its audience; for estimating the similarity of graphs, Jaccard similarity is used. Finally, for the Content similarity measurement, natural language processing techniques for preprocessing and TF-IDF for feature extraction are employed and then compared using the cosine similarity method. Results have presented the similarity level of different profiles. As the case study, people with the same interest show higher similarity. This way of comparison is helpful in many other areas. Also, it enables to find duplicate profiles; those are profiles with almost the same behavior and content.

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Detecting and Monitoring Hate Speech in Twitter

Sensors ◽

10.3390/s19214654 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4654 ◽

Cited By ~ 8

Author(s):

Juan Carlos Pereira-Kohatsu ◽

Lara Quijano-Sánchez ◽

Federico Liberatore ◽

Miguel Camacho-Collados

Keyword(s):

Social Media ◽

Hate Speech ◽

Intelligent System ◽

Negative Impact ◽

Area Under The Curve ◽

Hate Crimes ◽

Governmental Organizations ◽

Non Governmental Organizations ◽

National Office ◽

Public Dataset

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literature.

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HEALTH DEBATES ON SOCIAL MEDIA: LINKING DIGITAL HEALTH TO COMMUNICATION AND AI STUDIES

AoIR Selected Papers of Internet Research ◽

10.5210/spir.v2020i0.11268 ◽

2020 ◽

Author(s):

Martina Skrubbeltrang Mahnke ◽

Katrine Meldgaard Kjær

Keyword(s):

Social Media ◽

Communication Theory ◽

Digital Health ◽

Theoretical Perspective ◽

Health Data ◽

Digital Data ◽

Analytical Strategies ◽

Health Studies ◽

Theoretical Question ◽

Media Data

Drawing on the examples of three current health debates on Twitter revolving around the hashtags #medicalcannabis, #covid19 and #vaccinationervirker (in English: vaccinations work), this paper explores the broader theoretical question how we may expand the notion of ‘health data’ to include health debates and discussions on social media, and further how these can be linked to concepts of digital health data assemblages and communicative others. By combing insights from AI and communication studies and STS, as well as insights into the human-data relationship from digital health studies, the paper theoretically links digital data assemblages with communication theory which provides tools to think about health data as relational and communicative. With this, social media data becomes relevant in a new light, not only for media scientists, but also for understanding health practices in a digital age more generally. The paper discusses issues this theoretical perspective raises for researchers of social media and online health engagement; what challenges and possibilities this provides in relation to studying social media discussions on health; and finally, an overview of analytical strategies and empirical fields from which these perspectives may be studied.

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SocialRobot: a big data-driven humanoid intelligent system in social media services

Multimedia Systems ◽

10.1007/s00530-014-0374-0 ◽

2014 ◽

Vol 22 (1) ◽

pp. 17-27 ◽

Cited By ~ 4

Author(s):

Ting Liu ◽

Wei-Nan Zhang ◽

Yu Zhang

Keyword(s):

Social Media ◽

Big Data ◽

Intelligent System ◽

Data Driven ◽

Media Services

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COVID-19 pandemic impact on people with rheumatic and musculoskeletal diseases: Insights from patient-generated health data on social media

Rheumatology ◽

10.1093/rheumatology/keab174 ◽

2021 ◽

Author(s):

Katja Reuter ◽

Atul Deodhar ◽

Souzi Makri ◽

Michael Zimmer ◽

Francis Berenbaum ◽

...

Keyword(s):

Social Media ◽

Real Time ◽

Health Behaviour ◽

Lupus Erythematosus ◽

Negative Impact ◽

Musculoskeletal Diseases ◽

Health Data ◽

Physical And Mental Health ◽

Convenience Sample ◽

The Impact

Abstract Objectives During the COVID-19 pandemic, much communication occurred online, through social media. This study aimed to provide patient perspective data on how the COVID-19 pandemic impacted people with rheumatic and musculoskeletal diseases (RMDs), using Twitter-based patient-generated health data (PGHD). Methods A convenience sample of Twitter messages in English posted by people with RMDs was extracted between March 1, and July 12, 2020 and examined using thematic analysis. Included were Twitter messages that mentioned keywords and hashtags related to both COVID-19 (or SARS-CoV-2) and select RMDs. The RMDs monitored included inflammatory-driven (joint) conditions (Ankylosing Spondylitis, Rheumatoid Arthritis, Psoriatic Arthritis, Lupus/Systemic Lupus Erythematosus, and Gout). Results The analysis included 569 tweets by 375 Twitter users with RMDs across several countries. Eight themes emerged regarding the impact of the COVID-19 pandemic on people with RMDs: (1) lack of understanding of SARS-CoV-2/COVID-19; (2) critical changes in health behaviour; (3) challenges in healthcare practice and communication with healthcare professionals; (4) difficulties with access to medical care; (5) negative impact on physical and mental health, coping strategies; (6) issues around work participation, (7) negative effects of the media; (8) awareness-raising. Conclusion The findings show that Twitter serves as a real-time data source to understand the impact of the COVID-19 pandemic on people with RMDs. The platform provided “early signals” of potentially critical health behaviour changes. Future epidemics might benefit from the real-time use of Twitter-based PGHD to identify emerging health needs, facilitate communication, and inform clinical practice decisions.

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