Investigating the Impact of 9/11 on The Simpsons through Natural Language Processing

• URL: http://arxiv.org/abs/2112.03025v1
• Date: Thu, 2 Dec 2021 03:45:10 GMT
• Title: Investigating the Impact of 9/11 on The Simpsons through Natural Language Processing
• Authors: Athena Xiourouppa
• Abstract summary: The impact of real world events on fictional media is particularly apparent in the American cartoon series The Simpsons. Our aim was to search for changes in word frequency, topic, and sentiment before and after the September 11 terrorist attacks in New York. No clear trend change was seen, there was a slight decrease in the average sentiment over time around the relevant period between 2000 and 2002.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The impact of real world events on fictional media is particularly apparent in the American cartoon series The Simpsons. While there are often very direct pop culture references evident in the dialogue and visual gags of the show, subtle changes in tone or sentiment may not be so obvious. Our aim was to use Natural Language Processing to attempt to search for changes in word frequency, topic, and sentiment before and after the September 11 terrorist attacks in New York. No clear trend change was seen, there was a slight decrease in the average sentiment over time around the relevant period between 2000 and 2002, but the scripts still maintained an overall positive value, indicating that the comedic nature of The Simpsons did not wane particularly significantly. The exploration of other social issues and even specific character statistics is needed to bolster the findings here.

Related papers

• Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using LLMs [0.17999333451993949]
  This study uses Large Language Models (LLMs) to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024. Our findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. The results show a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes.
  arXiv  Detail & Related papers  (2025-01-20T00:44:38Z)
• Exploring the topics, sentiments and hate speech in the Spanish information environment [0.0]
  This study examines the topics, sentiments, and hate prevalence in 337,807 response messages (website comments and tweets) to news from five Spanish media outlets. Content is mainly negative (62.7%) and neutral (28.57%), with low positivity (8.73%) Despite low levels of hate speech (3.98%), the study confirms high toxicity in online responses to social and political topics.
  arXiv  Detail & Related papers  (2024-09-19T11:19:44Z)
• Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
  We propose a Dynamic Causal Disentanglement Model based on hidden variable separation. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
  arXiv  Detail & Related papers  (2023-09-13T12:58:09Z)
• Forgotten Knowledge: Examining the Citational Amnesia in NLP [63.13508571014673]
  We show how far back in time do we tend to go to cite papers? How has that changed over time, and what factors correlate with this citational attention/amnesia? We show that around 62% of cited papers are from the immediate five years prior to publication, whereas only about 17% are more than ten years old. We show that the median age and age diversity of cited papers were steadily increasing from 1990 to 2014, but since then, the trend has reversed, and current NLP papers have an all-time low temporal citation diversity.
  arXiv  Detail & Related papers  (2023-05-29T18:30:34Z)
• How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India [3.2296078260106174]
  The COVID-19 pandemic has claimed millions of lives worldwide and elicited heightened emotions. This study examines the expression of various emotions pertaining to COVID-19 in the United States and India as manifested in over 54 million tweets.
  arXiv  Detail & Related papers  (2023-03-19T04:05:10Z)
• Religion and Spirituality on Social Media in the Aftermath of the Global Pandemic [59.930429668324294]
  We analyse the sudden change in religious activities twofold: we create and deliver a questionnaire, as well as analyse Twitter data. Importantly, we also analyse the temporal variations in this process by analysing a period of 3 months: July-September 2020.
  arXiv  Detail & Related papers  (2022-12-11T18:41:02Z)
• COVID-19 sentiment analysis via deep learning during the rise of novel cases [0.5156484100374059]
  We use deep learning based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis on Twitter. We find that the majority of the tweets have been positive with high levels of optimism during the rise of the COVID-19 cases in India. We find that the optimistic and joking tweets mostly dominated the monthly tweets and there was a much lower number of negative sentiments expressed.
  arXiv  Detail & Related papers  (2021-04-05T04:31:19Z)
• Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time [0.0]
  We show how the topics associated with the development of the pandemic changed through time, using topic modeling. We show how the average subjectivity of the tweets increased linearly and fourth, how the popular and frequently used figurative frame of WAR changed when real riots and fights entered the discourse.
  arXiv  Detail & Related papers  (2021-03-16T10:22:39Z)
• Towards Debiasing Sentence Representations [109.70181221796469]
  We show that Sent-Debias is effective in removing biases, and at the same time, preserves performance on sentence-level downstream tasks. We hope that our work will inspire future research on characterizing and removing social biases from widely adopted sentence representations for fairer NLP.
  arXiv  Detail & Related papers  (2020-07-16T04:22:30Z)
• "Notic My Speech" -- Blending Speech Patterns With Multimedia [65.91370924641862]
  We propose a view-temporal attention mechanism to model both the view dependence and the visemic importance in speech recognition and understanding. Our proposed method outperformed the existing work by 4.99% in terms of the viseme error rate. We show that there is a strong correlation between our model's understanding of multi-view speech and the human perception.
  arXiv  Detail & Related papers  (2020-06-12T06:51:55Z)
• Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions [44.92240076313168]
  We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020. By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures. Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
  arXiv  Detail & Related papers  (2020-05-29T09:24:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.