Misogynistic Tweet Detection: Modelling CNN with Small Datasets

Bashar, Abul; Nayak, Richi; Suzor, Nicolas; Weir, Bridget

doi:10.1007/978-981-13-6661-1_1

Cited by 19 publications

(27 citation statements)

References 22 publications

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“…Studies Online detection [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [9], [39], [40], [41], [42], [43], [8], [44], [45], [46], [47], [48], [49] Offline detection [50], [51], [52], [53], [54], [7], [55], [56] Safety [57], [13], [58], [59], [60], [61],…”

Section: Categorymentioning

confidence: 99%

A Systematic Review of Computer Science Solutions for Addressing Violence Against Women and Children

et al. 2021

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Violence against women and children are public health issues of pandemic proportions. It is estimated that one in every three women worldwide have experienced physical, emotional or sexual violence. Similarly, each year one of two children are victims of some form of violence, including domestic aggression and bullying. Due to the widespread use of the Internet and social media, women and children are also now vulnerable to other types of violence such as cyber-bullying and online sexual or emotional harassment. To help alleviate these social problem, the use of computer sciences and related technologies has been leveraged in recent years. The Internet of Things, artificial intelligence, ubiquitous and mobile computing, pattern recognition, cloud computing, and similar technologies have been used to formulate solutions to detect and prevent violent acts. In this paper, a systematic review of some of the efforts that can help address the problem of violence against women and children is presented. This paper describes the current state-of-the-art of these contributions and identifies trends, architectures, technologies, and current open challenges. The survey was developed using a literature review of academic documents published from 2010 to 2020. The contributions were categorized in four application domains: online detection, offline detection, safety and education. Additionally, these contributions were further categorized based on the computer science approaches and technologies used: artificial intelligence, Internet of Things and digital serious games.

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Section: Categorymentioning

confidence: 99%

A Systematic Review of Computer Science Solutions for Addressing Violence Against Women and Children

et al. 2021

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“…Deep learning models have become quite successful in natural language processing, e.g., content generation, language translation, Question and Answering systems, text classification [8,7,10,9,2] and clustering. In spite of this success of DL in NLP, there has been very limited works on building a generic bias detection method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Followings can be inferred from Equation 6. (1) Each dataset D i relevant to the application domain of LM can reduce uncertainty [10,11]. (2) Pr-training of LSTM-LM should be done by the order of the dataset of general population distribution to the dataset of specific population distribution because the parameter vector ω i depends on ω i−1 [9,10].…”

Section: Neural Network Language Modelmentioning

confidence: 99%

“…Models with pretrained word vectors (i.e. word embeddings by word2Vec) include (1) LSTM with pretrained Word vectors (LSTM-W) [26] and (2) CNN with pretrained Word vectors (CNN-W) [11]. LSTM-W has 100 units, 50% dropout, binary cross-entropy loss function, Adam optimiser and sigmoid activation.…”

Section: Baseline Modelsmentioning

confidence: 99%

“…LSTM-W has 100 units, 50% dropout, binary cross-entropy loss function, Adam optimiser and sigmoid activation. The hyper parameters of CNN-W is set as in [11]. We used one million pretrained word vectors each with 300-dimension [37].…”

Section: Baseline Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Bias Detection: From Inception to Deployment

Bashar¹,

Nayak²,

Kothare³

et al. 2021

Preprint

Self Cite

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To create a more inclusive workplace, enterprises are actively investing in identifying and eliminating unconscious bias (e.g., gender, race, age, disability, elitism and religion) across their various functions. We propose a deep learning model with a transfer learning based language model to learn from manually tagged documents for automatically identifying bias in enterprise content. We first pretrain a deep learningbased language-model using Wikipedia, then fine tune the model with a large unlabelled data set related with various types of enterprise content. Finally, a linear layer followed by softmax layer is added at the end of the language model and the model is trained on a labelled bias dataset consisting of enterprise content. The trained model is thoroughly evaluated on independent datasets to ensure a general application. We present the proposed method and its deployment detail in a real-world application.

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Early breast cancer diagnosis using cogent activation function‐based deep learning implementation on screened mammograms

Rajput

Agrawal

Biyani

et al. 2022

Int J Imaging Syst Tech

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Breast cancer is detected in one out of eight females worldwide. Principally biomedical image processing techniques work with images captured by a microscope and then analyzed with the help of different algorithms and methods. Instead of microscopic image diagnosis, machine learning algorithms are now incorporated to detect and diagnose therapeutic imagery. Computeraided mechanisms are used for better efficiency and reliability compared with manual pathological detection systems. Machine learning algorithms detect tumors by extracting features through a convolutional neural network (CNN) and then classifying them using a fully connected network. As Machine learning does not require prior expertise, it is profoundly used in biomedical imaging. This article has customized a convolutional neural network by mathematical modeling of a proposed activation function. We have obtained an appreciable prediction accuracy of up to 99%, along with a precision of 0.97.

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Misogynistic Tweet Detection: Modelling CNN with Small Datasets

Cited by 19 publications

References 22 publications

A Systematic Review of Computer Science Solutions for Addressing Violence Against Women and Children

A Systematic Review of Computer Science Solutions for Addressing Violence Against Women and Children

Deep Learning for Bias Detection: From Inception to Deployment

Early breast cancer diagnosis using cogent activation function‐based deep learning implementation on screened mammograms

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