Real-Time Traffic Congestion Analysis Based on Collected Tweets

Al-qaness, Mohammed A. A.; Elaziz, Mohamed Abd; Hawbani, Ammar; Abbasi, Aaqif Afzaal; Zhao, Liang; Kim, Sunghwan

doi:10.1109/iucc/dsci/smartcns.2019.00030

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…They present a methodology for streaming, processing, and classifying public tweets by combining Natural Language Processing (NLP) techniques with a Support Vector Machine algorithm (SVM) for text classification. Furthermore, [2] used real-time Twitter data to analyse traffic congestion in Los Angeles, USA. The proposed model extracts traffic-related tweets and classifies the extracted information in order to estimate the traffic status on roads.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the deployment of such physical sensors is an inefficient solution for widespread tracking of traffic flows. Therefore, social media information regarding road and traffic conditions can be used as an alternative method for collecting traffic information [2]. This paper investigates the relationship between public Twitter data and transport network status, by comparing Twitter data geolocated near the location of a road incident or accident with those geolocated in parts of the network not close to such incident or accident locations.…”

Section: Introductionmentioning

confidence: 99%

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Public Twitter Data and Transport Network Status

Almohammad

Georgakis

2020

2020 10th International Conference on Information Science and Technology (ICIST)

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Twitter data can be collected and analysed to be used for predicting the status of a transport network at a given time and geographic location (e.g. forecasting disruptions, congestions, or road closures). However, this requires geolocating the tweets to define the parts of the transport network which may be related to these tweets. This paper investigates the relationship between the actual transport network status, with that being synthesised using public Twitter data in the Greater Manchester conurbation. Therefore, it answers the following question: are the sentiments of tweets around the incidents and accidents areas (or bounding boxes) different from the sentiments of tweets in the seamless traffic areas?. According to the used research methodology, analysis techniques, and sentiment detection APIs, it has been concluded that there is no significant difference between the sentiments in the tweets regardless the prevailing traffic conditions of the locations the tweets refer to.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Public Twitter Data and Transport Network Status

Almohammad

Georgakis

2020

2020 10th International Conference on Information Science and Technology (ICIST)

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“…The goal of this study is to analyze and to detect toxic behavior in Twitter using user-generated content in social media, such as Twitter. Twitter sentiment analysis has received wide attention, and has been utilized in various domains, for example, political influences [38][39][40], consumer insight mining [41], transportation services [42], movements of stock markets [43], traffic congestion detection [44,45], happiness evaluation [46], and others [47].…”

Section: Introductionmentioning

confidence: 99%

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Fan

Dahou

et al. 2021

Electronics

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Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.

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Emotion Analysis of Social Media Content for Mental Health Improvement

Benrouba

Boudour

2023

Lecture Notes in Networks and Systems

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Real-Time Traffic Congestion Analysis Based on Collected Tweets

Cited by 5 publications

References 15 publications

Public Twitter Data and Transport Network Status

Public Twitter Data and Transport Network Status

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Emotion Analysis of Social Media Content for Mental Health Improvement

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