Twitter Analysis for Intelligent Transportation

2018 ◽  
Vol 62 (11) ◽  
pp. 1547-1556
Author(s):  
Sarah Alhumoud
Keyword(s):  
Social Media ◽  
Survey Methods ◽  
Large Population ◽  
Detection Methods ◽  
Location Information ◽  
Social Media Mining ◽  
Proposed Model ◽  
Twitter Analysis ◽  
Main Components ◽  
Traffic Detection

Abstract The amount of data available online has grown enormously over the last decade as a result of the rapid growth of smartphone users and the availability of communication applications. Due to the anonymity and instantaneous nature of social media broadcasting compared to conventional attitudinal survey methods, social media mining is becoming popular for complementing traditional traffic detection methods due to its accessibility in reaching a large population and the opportunities for reflecting the true and immediate behaviour of participants for free. This study presents a framework for Arabic Twitter content analysis to gain transportation insight. The study is done with a dataset of more than 1 million tweets collected within 3 months. The proposed model comprises three main components: data acquisition, data analysis and the reverse geotagging scheme (RGS). The RGS tackles the problem of lack of location information in the tweets. Results show that 13% of the dataset reports traffic-related incidents with an overall precision of 55% and 87% for incidents identification prediction without and with reverse geotagging, respectively. This proves the efficiency of the developed analyser in identifying tweets on transportation and the potential of the RGS in defining the location of tweets with no registered location information.

scholarly journals A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

2021 ◽  
Vol 11 (17) ◽  
pp. 7940
Author(s):  
Mohammed Al-Sarem ◽  
Abdullah Alsaeedi ◽  
Faisal Saeed ◽  
Wadii Boulila ◽  
Omair AmeerBakhsh
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Superior Performance ◽  
Detection Methods ◽  
The Internet ◽  
Proposed Model ◽  
Deep Learning Model

Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score.

scholarly journals Taxonomy of Community Detection over Social Media

2019 ◽  
Vol 8 (11) ◽  
pp. 758-764
Keyword(s):  
Social Media ◽  
Social Network ◽  
Community Detection ◽  
Detection Methods ◽  
Social Media Mining ◽  
Network Nodes ◽  
Web Technologies ◽  
Mining Community ◽  
The Social ◽  
Media Mining

Advancements in web technologies in conjunction with the advent of social media facilitate online users to share contents and interact on a shared platform. Social media mining allows users to visualize, evaluate, analyze, and extract meaningful patterns and trends over the social network. Numerous methods and algorithms have been presented for the massive investigation of social media data. Community detection over social media is the most attracting field of interest for researchers in the area of social media mining. Community detection is a process of identifying densely connected network nodes and forming a group or community based on the density of interconnection among them. Detection of such communities is very crucial for a variety of applications in order to analyze the social network. This paper provides a brief introduction of social media, social media mining, and highlights prominent and recent research works done in the field of community detection. The paper presents the taxonomy of various algorithms and approaches for community detection over social media. The paper also includes in-depth details of extent community detection methods devised in the literature to detect communities over social media.

scholarly journals A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bocheng Wang
Keyword(s):  
Air Pollutants ◽  
Regression Models ◽  
Large Population ◽  
Vital Role ◽  
Superior Performance ◽  
Causal Direction ◽  
Northern Cities ◽  
Main Components ◽  
Surrounding Areas ◽  
Centrality Analysis

AbstractIn this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved.

scholarly journals “At Least until the Second Wave Comes…”: A Twitter Analysis of the NHS and COVID-19 between March and June 2020

2021 ◽  
Vol 18 (8) ◽  
pp. 3943
Author(s):  
Kathy McKay ◽  
Sarah Wayland ◽  
David Ferguson ◽  
Jane Petty ◽  
Eilis Kennedy
Keyword(s):  
Health Care ◽  
Social Media ◽  
Content Analysis ◽  
Well Being ◽  
Profound Impact ◽  
Time Period ◽  
Twitter Analysis ◽  
The Uk ◽  
Media Narratives ◽  
Second Wave

In the UK, tweets around COVID-19 and health care have primarily focused on the NHS. Recent research has identified that the psychological well-being of NHS staff has been adversely impacted as a result of the COVID-19 pandemic. The aim of this study was to investigate narratives relating to the NHS and COVID-19 during the first lockdown (26 March–4 July 2020). A total of 123,880 tweets were collated and downloaded bound to the time period of the first lockdown in order to analyse the real-time discourse around COVID-19 and the NHS. Content analysis was undertaken and tweets were coded to positive and negative sentiments. Five main themes were identified: (1) the dichotomies of ‘clap for carers’; (2) problems with PPE and testing; (3) peaks of anger; (4) issues around hero worship; and (5) hints of a normality. Further research exploring and documenting social media narratives around COVID-19 and the NHS, in this and subsequent lockdowns, should help in tailoring suitable support for staff in the future and acknowledging the profound impact that the pandemic has had.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

scholarly journals Gargantuan Hail in Argentina

2020 ◽  
Vol 101 (8) ◽  
pp. E1241-E1258 ◽  
Author(s):  
Matthew R. Kumjian ◽  
Rachel Gutierrez ◽  
Joshua S. Soderholm ◽  
Stephen W. Nesbitt ◽  
Paula Maldonado ◽  
...  
Keyword(s):  
Social Media ◽  
Large Population ◽  
Laser Scanner ◽  
Meteorological Conditions ◽  
Dimension Stone ◽  
World Record ◽  
Maximum Dimension ◽  
Supercell Storm ◽  
The World ◽  
Local Residents

Abstract On 8 February 2018, a supercell storm produced gargantuan (>15 cm or >6 in. in maximum dimension) hail as it moved over the heavily populated city of Villa Carlos Paz in Córdoba Province, Argentina. Observations of gargantuan hail are quite rare, but the large population density here yielded numerous witnesses and social media pictures and videos from this event that document multiple large hailstones. The storm was also sampled by the newly installed operational polarimetric C-band radar in Córdoba. During the RELAMPAGO campaign, the authors interviewed local residents about their accounts of the storm and uncovered additional social media video and photographs revealing extremely large hail at multiple locations in town. This article documents the case, including the meteorological conditions supporting the storm (with the aid of a high-resolution WRF simulation), the storm’s observed radar signatures, and three noteworthy hailstones observed by residents. These hailstones include a freezer-preserved 4.48-in. (11.38 cm) maximum dimension stone that was scanned with a 3D infrared laser scanner, a 7.1-in. (18 cm) maximum dimension stone, and a hailstone photogrammetrically estimated to be between 7.4 and 9.3 in. (18.8–23.7 cm) in maximum dimension, which is close to or exceeds the world record for maximum dimension. Such a well-observed case is an important step forward in understanding environments and storms that produce gargantuan hail, and ultimately how to anticipate and detect such extreme events.

scholarly journals Social media mining in drug development—fundamentals and use cases

2021 ◽  
Author(s):  
Jonathan Koss ◽  
Astrid Rheinlaender ◽  
Hubert Truebel ◽  
Sabine Bohnet-Joschko
Keyword(s):  
Social Media ◽  
Drug Development ◽  
Use Cases ◽  
Social Media Mining ◽  
Media Mining

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

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1332
Author(s):  
Hong Fan ◽  
Wu Du ◽  
Abdelghani Dahou ◽  
Ahmed A. Ewees ◽  
Dalia Yousri ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Modern Society ◽  
User Generated Content ◽  
Real World Application ◽  
Proposed Model ◽  
Social Media Platforms ◽  
Public Dataset ◽  
The Uk ◽  
Harmful Side Effect

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.

SOCIAL MEDIA MINING SHARED TASK WORKSHOP

2015 ◽  
Author(s):  
ABEED SARKER ◽  
AZADEH NIKFARJAM ◽  
GRACIELA GONZALEZ
Keyword(s):  
Social Media ◽  
Shared Task ◽  
Social Media Mining ◽  
Media Mining
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