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In the last decade, social networ ks have increasingl y been used in
social sciences to monitor consumer preferences and citizens’ opinion
formation, as they are able to produce a massive amount of data. In this paper, we aim to collect and analyse data from Twitter posts identifying emerging patterns related to the COVID-19 out break and to evalua...
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ABSTRACT
Purpose
The study analyzes Arabic Tweets, with a focus on those originating from the Gulf Cooperation Council (GCC) region, to examine public perceptions of the 2022 FIFA World Cup.
Methodology
Twitter’s search API was utilized to gather a substantial dataset of 157,756 tweets collected from 1 October 2022, to 1 January 2022. After data cl...
This research aims to know the description and result of the classification sentiment analysis by Twitter users about Shopee. The method used in this research is K-Nearest Neighbor. K-Nearest Neighbor is a method that identifies groups or classifications based on the closest k of test data (training data). The most relative distance is calculated u...
We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into h...
During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make com...
During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the first European countries to be severely affected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. W...
