An Ensemble Deep Learning Technique for Detecting Suicidal Ideation from Posts in Social Media Platforms

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

Download Full-text

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

10.4018/978-1-7998-8306-7.ch002 ◽

2022 ◽

pp. 20-39

Author(s):

Elliot Mbunge ◽

Benhildah Muchemwa

Keyword(s):

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Hospitality Industry ◽

Learning Models ◽

Online Data ◽

Social Media Platforms ◽

Using Data ◽

Tourism And Hospitality Industry ◽

Tourism And Hospitality

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

Download Full-text

Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16010054 ◽

2018 ◽

Vol 16 (1) ◽

pp. 54 ◽

Cited By ~ 3

Author(s):

Zheng Wang ◽

Guang Yu ◽

Xianyun Tian

Keyword(s):

Social Media ◽

Social Network ◽

Social Network Analysis ◽

Suicidal Ideation ◽

Network Analysis ◽

Online Communities ◽

Chinese Women ◽

Intervention Strategies ◽

Effective Prevention ◽

Social Media Platforms

People with suicidal ideation (PSI) are increasingly using social media to express suicidal feelings. Researchers have found that their internet-based communication may lead to the spread of suicidal ideation, which presents a set of challenges for suicide prevention. To develop effective prevention and intervention strategies that can be efficiently applied in online communities, we need to understand the behavior of PSI in internet-based communities. However, to date there have been no studies that specifically focus on the behavior of PSI in Chinese online communities. A total of 4489 postings in which users explicitly expressed their suicidal ideation were labeled from 560,000 postings in an internet-based suicidal community on Weibo (one of the biggest social media platforms in China) to explore their behavior. The results reveal that PSI are significantly more active than other users in the community. With the use of social network analysis, we also found that the more frequently users communicate with PSI, the more likely that users would become suicidal. In addition, Chinese women may be more likely to be at risk of suicide than men in the community. This study enriches our knowledge of PSI’s behavior in online communities, which may contribute to detecting and assisting PSI on social media.

Download Full-text

Detecting Rumors on Social Media Based on a CNN Deep Learning Technique

Arabian Journal for Science and Engineering ◽

10.1007/s13369-020-04839-2 ◽

2020 ◽

Vol 45 (12) ◽

pp. 10813-10844

Author(s):

Abdullah Alsaeedi ◽

Mohammed Al-Sarem

Keyword(s):

Social Media ◽

Deep Learning ◽

Learning Technique

Download Full-text

Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

ITM Web of Conferences ◽

10.1051/itmconf/20214003030 ◽

2021 ◽

Vol 40 ◽

pp. 03030

Author(s):

Mehdi Surani ◽

Ramchandra Mangrulkar

Keyword(s):

Social Media ◽

Deep Learning ◽

Freedom Of Speech ◽

Learning Approaches ◽

Learning Models ◽

Media Usage ◽

The Past ◽

Learning Techniques ◽

Social Media Platforms ◽

Machine Learning Models

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

Download Full-text

Protecting Geolocation Privacy of Photo Collections

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5390 ◽

2020 ◽

Vol 34 (01) ◽

pp. 524-531

Author(s):

Jinghan Yang ◽

Ayan Chakrabarti ◽

Yevgeniy Vorobeychik

Keyword(s):

Social Media ◽

Deep Learning ◽

Location Privacy ◽

User Behavior ◽

Personal Information ◽

Mixed Integer ◽

Individual Privacy ◽

Photo Collections ◽

Social Media Platforms ◽

Special Case

People increasingly share personal information, including their photos and photo collections, on social media. This information, however, can compromise individual privacy, particularly as social media platforms use it to infer detailed models of user behavior, including tracking their location. We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. One means to limit associated inadvertent geolocation privacy disclosure is by carefully pruning select photos from photo collections before these are posted publicly. We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning. We first demonstrate the complexity both by showing that a natural greedy algorithm can be arbitrarily bad and by proving that the problem is NP-Hard. We then exhibit an important tractable special case, as well as a more general approach based on mixed-integer linear programming. Through extensive experiments on real photo collections, we demonstrate that our approaches are indeed highly effective at preserving geolocation privacy.

Download Full-text

Analysis of Parkinson’s Disease using Deep Learning and Word Embedding Models

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.86 ◽

2019 ◽

Vol 2 (3) ◽

pp. 786-797

Author(s):

Feyza Cevik ◽

Zeynep Hilal Kilimci

Keyword(s):

Neural Networks ◽

Social Media ◽

Deep Learning ◽

Short Term Memory ◽

Learning Algorithms ◽

Economic Effects ◽

Word Embedding ◽

Social Media Platforms ◽

Accuracy Performance

Parkinson's disease is a common neurodegenerative neurological disorder, which affects the patient's quality of life, has significant social and economic effects, and is difficult to diagnose early due to the gradual appearance of symptoms. Examining the discussion of Parkinson&rsquo;s disease in social media platforms such as Twitter provides a platform where patients communicate each other in both diagnosis and treatment stage of the Parkinson&rsquo;s disease. The purpose of this work is to evaluate and compare the sentiment analysis of people about Parkinson's disease by using deep learning and word embedding models. To the best of our knowledge, this is the very first study to analyze Parkinson's disease from social media by using word embedding models and deep learning algorithms. In this study, Word2Vec, GloVe, and FastText are employed as word embedding models for the purpose of enriching tweets in terms of semantic, context, and syntax. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) are implemented for the classification task. This study demonstrates the efficiency of using word embedding models and deep learning algorithms to understand the needs of patients&rsquo; and provide a valuable contribution to the treatment process by analyzing sentiments of them with 93.63% accuracy performance.

Download Full-text

Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning

10.18653/v1/w18-6223 ◽

2018 ◽

Cited By ~ 13

Author(s):

Ramit Sawhney ◽

Prachi Manchanda ◽

Puneet Mathur ◽

Rajiv Shah ◽

Raj Singh

Keyword(s):

Social Media ◽

Deep Learning ◽

Suicidal Ideation

Download Full-text

Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic

Frontiers in Computer Science ◽

10.3389/fcomp.2021.775368 ◽

2021 ◽

Vol 3 ◽

Author(s):

Ram Krishn Mishra ◽

Siddhaling Urolagin ◽

J. Angel Arul Jothi ◽

Ashwin Sanjay Neogi ◽

Nishad Nawaz

Keyword(s):

Social Media ◽

Deep Learning ◽

Topic Modeling ◽

Tourism Industry ◽

Classification Model ◽

Training Set ◽

Test Set ◽

Multiple Parameters ◽

Social Media Platforms ◽

Flow Of Information

The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user’s emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people’s feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.

Download Full-text

Instagram Use and the Well-Being of Adolescents: Using Deep Learning to Link Social Scientific Self-reports with Instagram Data Download Packages

10.31235/osf.io/r6atb ◽

2020 ◽

Author(s):

Laura Boeschoten ◽

Irene Ingeborg van Driel ◽

Daniel L. Oberski ◽

J. Loes Pouwels

Keyword(s):

Social Media ◽

Deep Learning ◽

Well Being ◽

Self Report ◽

Interdisciplinary Project ◽

Self Reports ◽

Social Media Platforms ◽

Report Data ◽

Social Scientific ◽

Self Report Data

Since the introduction of social media platforms, researchers have investigated how the use of such media affects adolescents’ well-being. Thus far, findings have been inconsistent. The aim of our interdisciplinary project is to provide a more thorough understanding of these inconsistencies by investigating who benefits from social media use, who does not and why it is beneficial for one yet harmful for another. In this presentation, we explain our approach to combining social scientific self-report data with the use of deep learning to analyze personal Instagram archives.

Download Full-text