Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset

Mohbey, Krishna Kumar

doi:10.1007/s42488-019-00013-y

Cited by 24 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, analyzing the behavior of the users in an OSN is a complex task [8] , and thus, some models to detect anomalies in the user behavior have been studied [9] . Some studies have focused particularly on the domain of user behavior analysis on social media for instance in the contexts of political events [10] , [11] , a diverse range of recommendation systems [12] – [14] , public health [2] , [15] , communication network recommendations [16] , and prediction of urban traffic trends [12] , [17] , among others.…”

Section: Introductionmentioning

confidence: 99%

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Rosa

Silva

et al. 2020

IEEE Access

View full text Add to dashboard Cite

People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users' behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users' behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user's location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages. INDEX TERMS Event detection, Online Social Networks, affective analysis, natural language processing, COVID-19.

show abstract

Section: Introductionmentioning

confidence: 99%

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Rosa

Silva

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Twitter is a popular micro-blogging website that offers the users to send and interact with short messages. Twitter was also chosen as a data source of much research due to its popularity, ease of use to express opinions, and use of its tweets up to 280 characters (Bouazizi & Ohtsuki, 2019;Mohbey, 2020;Tane et al, 2019)…”

Section: Twittermentioning

confidence: 99%

Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking

Candrika,

Jondri,

Indwiarti

2023

jinav j. inf. vis.

View full text Add to dashboard Cite

The rapid development of technology from day to day indirectly influences increasing social media use. This can be seen from spreading information that is very easily found on social media, one of which is Twitter. It is one of the most popular platforms for expressing people’s feelings by tweeting and interacting with other users at the same time. Various opinions about the COVID-19 vaccination began to be discussed on the Twitter platform. Moreover, most people take advantage of the feature available on Twitter, namely retweets. Users do retweet because there are many influencing factors. It can be caused by a reason that they have the same opinions and thoughts as the tweet owner, and so on. A retweet feature is also a form of information diffusion on the Twitter platform. The diffusion of information on Twitter has several factors, such as the most influential users, using hashtags or URLs, and others. In this conclusion, retweet predictions have been carried out regarding COVID-19 vaccination tweets using the features user-based and time-based through the Multi-Level Stacking classification method. This method indicates the best results when oversampling with an F1-Score of 96.23%.

show abstract

“…It is known that a person in stress is bound to do things in rage [21]. The work in [21] uses this property, to identify the mental state of the user by their keyboard typing patterns. They have used smartphone keyboards and physical keyboards in order to find out if the user is in stress or not.…”

Section: Analysis Of Parameters For Human Behavioural Predictionmentioning

confidence: 99%

“…As mentioned in [16] social behaviour plays a very important role in predicting user behaviour. The work in [22] extends this concept by taking election user tweet data, and then combines it with deep learning-based classifier to evaluate user behaviour patterns. These patterns are more focussed towards election scenarios, but by changing the dataset, the same concept can be applied to any kind of human behaviour.…”

Section: Analysis Of Parameters For Human Behavioural Predictionmentioning

confidence: 99%

Human Behavior Prediction and Analysis Using Machine Learning-A Review

Gulhane¹

2021

TURCOMAT

View full text Add to dashboard Cite

Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.

show abstract

Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset

Cited by 24 publications

References 43 publications

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking

Human Behavior Prediction and Analysis Using Machine Learning-A Review

Contact Info

Product

Resources

About