scholarly journals Detection of Depression in Thai Social Media Messages using Deep Learning

2020 ◽  
Author(s):  
Boriharn Kumnunt ◽  
Ohm Sornil
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Media Messages ◽  
Detection Of Depression

Deep Learning Approach for the Detection of Depression in Twitter

2019 ◽  
Author(s):  
Aswathy K S ◽  
Rafeeque P C ◽  
Reena Murali
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Detection Of Depression

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Steganographic visual story with mutual-perceived joint attention

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yanyang Guo ◽  
Hanzhou Wu ◽  
Xinpeng Zhang
Keyword(s):  
Social Support ◽  
Social Media ◽  
Deep Learning ◽  
Joint Attention ◽  
Online Social Networks ◽  
Generation Model ◽  
High Quality ◽  
Visual Storytelling ◽  
The Social ◽  
User Intervention

AbstractSocial media plays an increasingly important role in providing information and social support to users. Due to the easy dissemination of content, as well as difficulty to track on the social network, we are motivated to study the way of concealing sensitive messages in this channel with high confidentiality. In this paper, we design a steganographic visual stories generation model that enables users to automatically post stego status on social media without any direct user intervention and use the mutual-perceived joint attention (MPJA) to maintain the imperceptibility of stego text. We demonstrate our approach on the visual storytelling (VIST) dataset and show that it yields high-quality steganographic texts. Since the proposed work realizes steganography by auto-generating visual story using deep learning, it enables us to move steganography to the real-world online social networks with intelligent steganographic bots.

scholarly journals 1414. #Vaccine Twitter Influencers: is it Just About Reach and Followers?

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S714-S715
Author(s):  
Jean-Etienne Poirrier ◽  
Theodore Caputi ◽  
John Ayers ◽  
Mark Dredze ◽  
Sara Poston ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Standard Deviation ◽  
Small Proportion ◽  
The United States ◽  
Research Support ◽  
Policy Makers ◽  
Media Messages ◽  
Social Media Platforms ◽  
The Impact

Abstract Background A small number of powerful users (“influencers”) dominates conversations on social media platforms: less than 1% of Twitter accounts have at least 3,000 followers and even fewer have hundreds of thousands or millions of followers. Beyond simple metrics (number of tweets, retweets...) little is known about these “influencers”, particularly in relation to their role in shaping online narratives about vaccines. Our goal was to describe influential Twitter accounts that are driving conversations about vaccines and present new metrics of influence. Methods Using publicly-available data from Twitter, we selected posts from 1-Jan-2016 to 31-Dec-2018 and extracted the top 5% of accounts tweeting about vaccines with the most followers. Using automated classifiers, we determined the location of these accounts, and grouped them into those that primarily tweet pro- versus anti-vaccine content. We further characterized the demographics of these influencer accounts. Results From 25,381 vaccine-related tweets available in our sample representing 10,607 users, 530 accounts represented the top 5% by number of followers. These accounts had on average 1,608,637 followers (standard deviation=5,063,421) and 340,390 median followers. Among the accounts for which sentiment was successfully estimated by the classifier, 10.4% (n=55) posted anti-vaccine content and 33.6% (n=178) posted pro-vaccine content. Of the 55 anti-vaccine accounts, 50% (n=18) of the accounts for which location was successfully determined were from the United States. Of the 178 pro-vaccine accounts, 42.5% (n=54) were from the United States. Conclusion This study showed that only a small proportion of Twitter accounts (A) post about vaccines and (B) have a high follower count and post anti-vaccine content. Further analysis of these users may help researchers and policy makers better understand how to amplify the impact of pro-vaccine social media messages. Disclosures Jean-Etienne Poirrier, PhD, MBA, The GSK group of companies (Employee, Shareholder) Theodore Caputi, PhD, Good Analytics Inc. (Consultant) John Ayers, PhD, GSK (Grant/Research Support) Mark Dredze, PhD, Bloomberg LP (Consultant)Good Analytics (Consultant) Sara Poston, PharmD, The GlaxoSmithKline group of companies (Employee, Shareholder) Cosmina Hogea, PhD, GlaxoSmithKline (Employee, Shareholder)

Deep learning for Antisocial Behaviour Analysis on Social Media

2020 ◽  
Author(s):  
Ravinder Singh ◽  
Yanchun Zhang ◽  
Hua Wang ◽  
Yuan Miao ◽  
Khandakar Ahmed
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Antisocial Behaviour ◽  
Behaviour Analysis

SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media

2021 ◽  
Author(s):  
Yuheng Hu ◽  
Yili Hong
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Event Detection ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Neural Network Models ◽  
Neural Event ◽  
End To End

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.

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