Looking Behind the Mask: A framework for Detecting Character Assassination via Troll Comments on Social media using Psycholinguistic Tools

Marouf, Ahmed Al; Ajwad, Rasif; Ashrafi, Adnan Ferdous

doi:10.1109/ecace.2019.8679154

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Amalia Amalia et al, in categorizing Indonesian government-based meme [1] used recognition of optical characters with Tesseract for the process of recognizing character through English alphabet dictionary [2] which transforms character in meme to be readable by machine which by Natural Language Processing (NLP) and Image Processing [3] to be applied on text separated images is fed to Naive Bayes which correctly categorizes up to 75%. Few tokens are not detected correctly by this approach.…”

Section: Literature Surveymentioning

confidence: 99%

Troll Meme Classification for Tamil Memes Using Convolutional Neural Network

Nithya¹,

Sathyapriya²,

Sulochana³

et al. 2023

ACE

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Memes have become a new type of internet communication. It has the ability to instantly disseminate anger, offensiveness, and violence. Because of its regional meaning, classifying memes is difficult.. This work presents here a computational model of classifying Tamil memes using convolutional neural networks. Convolutional neural networks have the potential to learn, adapt, and rearrange themselves. As a result, it can extract features automatically applying prior knowledge of existing categories, avoiding the time-consuming feature extraction process used in older methods in images. The basic layer of MobileNet is made up of depth-wise separable filters, also referred to as depth-wise separable convolution. The network structure is another feature that boosts performance. It utilizes very less than computation power while applying transfer learning. This network has reduced parameters and computation cost. Skip connections, or shortcuts, are used by residual neural networks to jump past some layers. Residual connections allow parameter gradients to travel more easily from the output layer to the network's prior layers, allowing for the training of deeper networks. Higher accuracies on more demanding tasks may arise from the greater network depth. AlexNet is a leading architecture for any image identification task, and it could have a lot of applications in the artificial intelligence field of computer vision. In the future, AlexNet may be used for image classification jobs more than CNNs. This work aims to classify Tamil memes using Mobilenet, Resnet and hyper parameter turned AlexNet.

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Section: Literature Surveymentioning

confidence: 99%

Troll Meme Classification for Tamil Memes Using Convolutional Neural Network

Nithya¹,

Sathyapriya²,

Sulochana³

et al. 2023

ACE

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“…Mihaylov and colleagues conducted a similar study using community rating, topic consistency, order of comments, answers and time of the day to predict troll users in an online newspaper community [43]. Moreover, Al Marouf [5] tried to create a profile of personality traits with user’s textual data, while Fornacciari [44] tried to detect and predict troll users in social media through a sentiment analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the academia, agro has been quoted in studies of Korean neologism [3][4][5][6][7], and online Korean fan community behaviour [8]. It has been more ardently discussed in Korean news articles, in which journalists have continuously pointed out the adverse social effects of agro [9].…”

Section: Introductionmentioning

confidence: 99%

Detecting agro: Korean trolling and clickbaiting behaviour in online environments

Choi

Kim²,

Jeong

et al. 2022

Journal of Information Science

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This article presents one of the first approaches to provide the understanding of agro (one of the unique eye-attracting cues) headlines and thumbnails in online video sharing platform, YouTube. We annotated 1881 headlines and thumbnails, based on agro and the type of agro. Then, we experimented with machine learning models to classify agro data from the non- agro data. With a bidirectional long short-term memory (Bi-LSTM) model, we achieved 84.35% of accuracy in detecting agro headlines and 82.80% of accuracy in detecting agro thumbnails. We believe that the automatic detection of agro headlines can allow users to have better experience in browsing through and getting the content that they want online.

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“…An interesting work giving a proper overview on psycholinguistic data extraction is the detection of character assassination attempts by troll actions, performed by El Marouf et al [35]. Six different feature extraction tools were used in the construction of the dataset for supervised learning.…”

Section: Psycholinguistic Featuresmentioning

confidence: 99%

“…Although an audit has revealed that the software to generate deepfake videos is still hard to use 32 , there is an increase in such fake contents, not only affecting celebrities 33 , but also less-known people 34,35 . As some authors have previously stated, technology will play a keystone role in fighting deepfakes [103].…”

Section: The Emergence Of Deepfakesmentioning

confidence: 99%

Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study

Choraś¹,

Demestichas²,

Giełczyk³

et al. 2021

Preprint

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Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It starts by showing the historical perspective and the current role of fake news in the information war. Proposed solutions based solely on the work of experts are analysed and the most important directions of the application of intelligent systems in the detection of misinformation sources are pointed out. Additionally, the paper presents some useful resources (mainly datasets useful when assessing ML solutions for fake news detection) and provides a short overview of the most important R&D projects related to this subject. The main purpose of this work is to analyse the current state of knowledge in detecting fake news; on the one hand to show possible solutions, and on the other hand to identify the main challenges and methodological gaps to motivate future research.

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Looking Behind the Mask: A framework for Detecting Character Assassination via Troll Comments on Social media using Psycholinguistic Tools

Cited by 6 publications

References 13 publications

Troll Meme Classification for Tamil Memes Using Convolutional Neural Network

Troll Meme Classification for Tamil Memes Using Convolutional Neural Network

Detecting agro: Korean trolling and clickbaiting behaviour in online environments

Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study

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