Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

Rita, Paulo; António, Nuno; Afonso, Ana Patrícia

doi:10.1007/s13278-023-01048-1

Cited by 14 publications

(7 citation statements)

References 46 publications

(61 reference statements)

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“…This approach analyzes how discourses produce and reproduce power structures (Ramanathan & Hoon, 2015). Media discourse refers to the way in which media outlets, such as newspapers, television, and the internet, present and construct meaning about particular topics or issues (Rita et al, 2023). It encompasses the language, images, and other forms of representation used in these media, as well as the ways in which these representations are organized and presented (Barkemeyer et al, 2018).…”

Section: Methodology Discourse Analysismentioning

confidence: 99%

Digital media’s portrayal of climate change challenges during COP27 climate summit

Radwan,

Ayyad

2024

ONLINE J COMMUN MEDIA TECHNOL

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The study aims to investigate how digital media frame and present environmental issues and challenges of climate change in terms of four environmental and climate challenges: mitigation, adaptation, finance, and collaboration. The study also seeks to understand the extent that government bodies mobilize digital media to disseminate and promote necessary environmental challenges during COP27 climate summit in Egypt. The study uses the discourse analysis approach to define the key themes and issues that are portrayed and to understand how digital platforms reflected the conference agenda and climate change challenges. We analyzed 119 posts and news reports published on the conference website and Instagram account during November 2022. Results revealed that the platforms framed challenges of climate change during COP27 in many ways: the opportunity to find solutions, the urgency to address climate issues, and taking immediate actions in terms of transparency, adequate finance, ease of support flow, cooperation, participation, and youth involvement. Furthermore, the data indicate that the finance challenge has a strong relationship with all themes and challenges presented and discussed on the platforms. This indicates that African countries benefited from hosting COP27 by presenting their environmental concerns, the financial, and technological challenges they face in order to obtain global support.

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Section: Methodology Discourse Analysismentioning

confidence: 99%

Digital media’s portrayal of climate change challenges during COP27 climate summit

Radwan,

Ayyad

2024

ONLINE J COMMUN MEDIA TECHNOL

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“…For each observation x i , find the closest cluster centroid and assign x i to that cluster, as shown in Equation (1).…”

Section: • Assignment Stepmentioning

confidence: 99%

“…Over the past two decades, social media has assumed a critical and transformative role in the daily lives of individuals worldwide. Current data reveal that a third of the global population engages with at least one social networking site for various purposes, particularly among the youth who, on average, spend over four hours online daily [1][2][3]. This surge in technology usage has necessitated adjustments to the emerging virtual landscape.…”

Section: Introductionmentioning

confidence: 99%

Boosting Institutional Identity on X Using NLP and Sentiment Analysis: King Faisal University as a Case Study

Albarrak,

Sorour

2024

Mathematics

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Universities increasingly leverage social media platforms, especially Twitter, for news dissemination, audience engagement, and feedback collection. King Faisal University (KFU) is dedicated to enhancing its institutional identity (ID), grounded in environmental sustainability and food security, encompassing nine critical areas. This study aims to assess the impact of KFU’s Twitter interactions on public awareness of its institutional identity using systematic analysis and machine learning (ML) methods. The objectives are to: (1) Determine the influence of KFU’s Twitter presence on ID awareness; (2) create a dedicated dataset for real-time public interaction analysis with KFU’s Twitter content; (3) investigate Twitter’s role in promoting KFU’s institutional identity across 9-ID domains and its changing impact over time; (4) utilize k-means clustering and sentiment analysis (TFIDF and Word2vec) to classify data and assess similarities among the identity domains; and (5) apply the categorization method to process and categorize tweets, facilitating the assessment of word meanings and similarities of the 9-ID domains. The study also employs four ML models, including Logistic Regression (LR) and Support Vector Machine (SVM), with the Random Forest (RF) model combined with Word2vec achieving the highest accuracy of 100%. The findings underscore the value of KFU’s Twitter data analysis in deepening the understanding of its ID and guiding the development of effective communication strategies.

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“…Besides social media -but in some cases through social media posts -SA found applications in domains such as healthcare [11][12][13][14], politics [15][16][17], public policy [18], psychology [19,20], marketing [21], scientific citations [22] business [23][24][25] and finance [26] and so on. In [27], the authors formulated a taxonomy of research topics in which the interested reader can seek additional information on SA application domains.…”

Section: Introductionmentioning

confidence: 99%

Financial sentiment analysis: Classic methods vs. deep learning models

Karanikola,

Davrazos,

Liapis

et al. 2023

IDT

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Sentiment Analysis, also known as Opinion Mining, gained prominence in the early 2000s alongside the emergence of internet forums, blogs, and social media platforms. Researchers and businesses recognized the imperative to automate the extraction of valuable insights from the vast pool of textual data generated online. Its utility in the business domain is undeniable, offering actionable insights into customer opinions and attitudes, empowering data-driven decisions that enhance products, services, and customer satisfaction. The expansion of Sentiment Analysis into the financial sector came as a direct consequence, prompting the adaptation of powerful Natural Language Processing models to these contexts. In this study, we rigorously test numerous classical Machine Learning classification algorithms and ensembles against five contemporary Deep Learning Pre-Trained models, like BERT, RoBERTa, and three variants of FinBERT. However, its aim extends beyond evaluating the performance of modern methods, especially those designed for financial tasks, to a comparison of them with classical ones. We also explore how different text representation and data augmentation techniques impact classification outcomes when classical methods are employed. The study yields a wealth of intriguing results, which are thoroughly discussed.

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Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

Cited by 14 publications

References 46 publications

Digital media’s portrayal of climate change challenges during COP27 climate summit

Digital media’s portrayal of climate change challenges during COP27 climate summit

Boosting Institutional Identity on X Using NLP and Sentiment Analysis: King Faisal University as a Case Study

Financial sentiment analysis: Classic methods vs. deep learning models

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