scholarly journals An experimental study on feature engineering and learning approaches for aggression detection in social media

2019 ◽  
Vol 22 (63) ◽  
pp. 81-100 ◽  
Author(s):  
Antonela Tommasel ◽  
Juan Manuel Rodriguez ◽  
Daniela Godoy
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Social Relations ◽  
Large Scale ◽  
Well Being ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Social Media Networks ◽  
Online Social Media ◽  
Learning Techniques

With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to aggressive and intentional acts aiming at repeatedly causing harm to other person involving rude, insulting, offensive, teasing or demoralising comments through online social media. As these aggressions represent a threatening experience to Internet users, especially kids and teens who are still shaping their identities, social relations and well-being, it is crucial to understand how cyberbullying occurs to prevent it from escalating. Considering the massive information on the Web, the developing of intelligent techniques for automatically detecting harmful content is gaining importance, allowing the monitoring of large-scale social media and the early detection of unwanted and aggressive situations. Even though several approaches have been developed over the last few years based both on traditional and deep learning techniques, several concerns arise over the duplication of research and the difficulty of comparing results. Moreover, there is no agreement regarding neither which type of technique is better suited for the task, nor the type of features in which learning should be based. The goal of this work is to shed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressions in social media texts. In this context, this work provides an evaluation of diverse traditional and deep learning techniques based on diverse sets of features, across multiple social media sites. 

scholarly journals Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

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.

scholarly journals TreeVibes: Modern tools for Global Monitoring of Trees Against Borers

2020 ◽  
Author(s):  
Iraklis Rigakis ◽  
Ilyas Potamitis ◽  
Nicolas Alexander Tatlas ◽  
Stelios M. Potirakis ◽  
Stavros Ntalampiras
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Cost Effective ◽  
Automatic Monitoring ◽  
Urban Environments ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Longhorn Beetles ◽  
Cloud Server ◽  
Wood Boring

Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways on how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside wood. The recordings come from remotely controlled devices that sample the internal soundscape of trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to a cloud server. We discuss the different sources of vibrations that can be picked up from trees in urban environments and how deep learning methods can focus on those originating from borers. Our goal is to match the problem of the accelerated—due to global trade and climate change— establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. We aim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learning techniques, in commodity entry point as well as in wild, urban and cultivated areas in order to effect large-scale, sustainable pest-risk analysis and management of wood boring insects such as those from the Cerambycidae family (longhorn beetles).

scholarly journals TreeVibes: Modern Tools for Global Monitoring of Trees for Borers

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 271-285
Author(s):  
Iraklis Rigakis ◽  
Ilyas Potamitis ◽  
Nicolaos-Alexandros Tatlas ◽  
Stelios M. Potirakis ◽  
Stavros Ntalampiras
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Cost Effective ◽  
Automatic Monitoring ◽  
Urban Environments ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Longhorn Beetles ◽  
Entry Points ◽  
Wood Boring

Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways of how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside wood. The recordings come from remotely controlled devices that sample the internal soundscape of trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to a cloud server. We discuss the different sources of vibrations that can be picked up from trees in urban environments and how deep learning methods can focus on those originating from borers. Our goal is to match the problem of the accelerated—due to global trade and climate change— establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. We aim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learning techniques, in commodity entry points as well as in wild, urban and cultivated areas in order to effect large-scale, sustainable pest-risk analysis and management of wood boring insects such as those from the Cerambycidae family (longhorn beetles).

scholarly journals Applying Deep Learning Technique for Depression Classification in Social Media Text

2020 ◽  
Vol 10 (10) ◽  
pp. 2446-2451
Author(s):  
Hussain Ahmad ◽  
Muhammad Zubair Asghar ◽  
Fahad M. Alotaibi ◽  
Ibrahim A. Hameed
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Large Scale ◽  
Research Area ◽  
Mental Illnesses ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Close Relationship ◽  
Social Media Platforms

In social media, depression identification could be regarded as a complex task because of the complicated nature associated with mental disorders. In recent times, there has been an evolution in this research area with growing popularity of social media platforms as these have become a fundamental part of people's day-to-day life. Social media platforms and their users share a close relationship due to which the users' personal life is reflected in these platforms on several levels. Apart from the associated complexity in recognising mental illnesses via social media platforms, implementing supervised machine learning approaches like deep neural networks is yet to be adopted in a large scale because of the inherent difficulties associated with procuring sufficient quantities of annotated training data. Because of such reasons, we have made effort to identify deep learning model that is most effective from amongst selected architectures with previous successful record in supervised learning methods. The selected model is employed to recognise online users that display depression; since there is limited unstructured text data that could be extracted from Twitter.

scholarly journals Social media and deep learning capture the aesthetic quality of the landscape

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ilan Havinga ◽  
Diego Marcos ◽  
Patrick W. Bogaart ◽  
Lars Hein ◽  
Devis Tuia
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Large Scale ◽  
Survey Methods ◽  
Well Being ◽  
Point Of View ◽  
Aesthetic Quality ◽  
Large Scale Analysis ◽  
Aesthetic Enjoyment ◽  
The Aesthetic

AbstractPeoples’ recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples’ well-being in support of sustainable policy goals. However, the survey methods available to measure these contributions restrict modelling at large scales. As a result, most studies rely on environmental indicator models but these do not incorporate peoples’ actual use of the landscape. Now, social media has emerged as a rich new source of information to understand human-nature interactions while advances in deep learning have enabled large-scale analysis of the imagery uploaded to these platforms. In this study, we test the accuracy of Flickr and deep learning-based models of landscape quality using a crowdsourced survey in Great Britain. We find that this novel modelling approach generates a strong and comparable level of accuracy versus an indicator model and, in combination, captures additional aesthetic information. At the same time, social media provides a direct measure of individuals’ aesthetic enjoyment, a point of view inaccessible to indicator models, as well as a greater independence of the scale of measurement and insights into how peoples’ appreciation of the landscape changes over time. Our results show how social media and deep learning can support significant advances in modelling the aesthetic contributions of ecosystems for ES assessments.

scholarly journals Fake News Detector in Online Social Media

2019 ◽  
Vol 9 (1S4) ◽  
pp. 58-60
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Fake News ◽  
The Public ◽  
Online Social Media ◽  
Learning Techniques ◽  
State Of Art

Spreading of fake news in online social media is a major nuisance to the public and there is no state of art tool to detect whether a news is a fake or an original one in an automated manner. Hence, this paper analyses the online social media and the news feeds for detection of fake news. The work proposes solution using Natural Language Processing and Deep Learning techniques for detecting the fake news in online social media.

Concrete Monsters of the Welfare State: Discussions of Brutalist Architecture on Social Media

2021 ◽  
pp. 120633122110193
Author(s):  
Max Holleran
Keyword(s):  
Social Media ◽  
Welfare State ◽  
Large Scale ◽  
Eastern European ◽  
Municipal Governments ◽  
Online Social Media ◽  
1960S And 1970S ◽  
The 1960S ◽  
The Aesthetic ◽  
The Welfare State

Brutalist architecture is an object of fascination on social media that has taken on new popularity in recent years. This article, drawing on 3,000 social media posts in Russian and English, argues that the buildings stand out for their arresting scale and their association with the expanding state in the 1960s and 1970s. In both North Atlantic and Eastern European contexts, the aesthetic was employed in publicly financed urban planning projects, creating imposing concrete structures for universities, libraries, and government offices. While some online social media users associate the style with the overreach of both socialist and capitalist governments, others are more nostalgic. They use Brutalist buildings as a means to start conversations about welfare state goals of social housing, free university, and other services. They also lament that many municipal governments no longer have the capacity or vision to take on large-scale projects of reworking the built environment to meet contemporary challenges.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

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