Analysis of foul language usage in social media text conversation

Kawate, Sumit; Patil, Kailas

doi:10.1504/ijsmile.2017.087976

Cited by 6 publications

(2 citation statements)

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“…"Scoundrel" Keywords related to swearing, that represent emotions. These emotional expressions maintain a negative correlation with economic activity (Stephens and Zile, 2017│ Kawate and Patil, 2017│ Celdrán, 2008.…”

Section: Variablesmentioning

confidence: 99%

A Proposal of a Real Time Economic Sentiment Indicator Based on Twitter and Google Trends for the Spanish Economy

Poza¹,

Lazcano²,

Monge³

2022

R. Econ. and Finan.

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The main aim of this paper is to build a real time economic sentiment indicator (RT-ESI) for Spain, based on text mining and deep learning from Twitter and Google Trends, that can anticipate GDP and household consumer behaviour. This work contributes to the literature, firstly by carrying out a sentiment analysis with a set of selected keywords that are related to emotions and expectations, then we apply a factor analysis to create the composite indicator, next we use a descriptive analysis to highlight the main associations between indexes, and finally we employ fractional integration and cointegration techniques (ARFIMA and FCVAR) to assess the RT-ESI behaviour against the European Commission´s consumer confidence indicator and the GDP. The results show that the GDP (YoY) presents the same behaviour as our leading indicator, finding mean reversion. The behaviour of the CCI series differs from the others.

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Section: Variablesmentioning

confidence: 99%

A Proposal of a Real Time Economic Sentiment Indicator Based on Twitter and Google Trends for the Spanish Economy

Poza¹,

Lazcano²,

Monge³

2022

R. Econ. and Finan.

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“…With the increased use of social media platform like Twitter, Facebook, Instagram, and YouTube by users around the world, the platforms have had positive aspects including but not limited to social interaction, meeting like-minded people, giving a voice to each individual to share their opinions [1]. However, as a result, social media platforms can also be used to spread hate comments, hate posts by certain individuals or groups; which can lead to having anxiety, mental illness and severe stress to people who consume that hate content [2]. It becomes necessary to be able to detect such activities at its earliest to stop it from spreading, thereby making social media a healthy place to interact and share their views without a fear of getting hate comments [3].…”

Section: Introductionmentioning

confidence: 99%

Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss

Mitra¹,

Sankhala²

2022

Preprint

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The number of increased social media users has led to a lot of people misusing these platforms to spread offensive content and use hate speech. Manual tracking the vast amount of posts is impractical so it is necessary to devise automated methods to identify them quickly. Large language models are trained on a lot of data and they also make use of contextual embeddings. We fine-tune the large language models to help in our task. The data is also quite unbalanced; so we used a modified cross-entropy loss to tackle the issue. We observed that using a model which is fine-tuned in hindi corpora performs better. Our team (HNLP) achieved the macro F1-scores of 0.808, 0.639 in English Subtask A and English Subtask B respectively. For Hindi Subtask A, Hindi Subtask B our team achieved macro F1-scores of 0.737, 0.443 respectively in HASOC 2021.

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Dataset Construction for the Detection of Anti-Social Behaviour in Online Communication in Arabic

Alakrot

Murray

Nikolov

2018

Procedia Computer Science

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Analysis of foul language usage in social media text conversation

Cited by 6 publications

References 0 publications

A Proposal of a Real Time Economic Sentiment Indicator Based on Twitter and Google Trends for the Spanish Economy

A Proposal of a Real Time Economic Sentiment Indicator Based on Twitter and Google Trends for the Spanish Economy

Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss

Dataset Construction for the Detection of Anti-Social Behaviour in Online Communication in Arabic

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