We Tweet Like We Talk and Other Interesting Observations: An Analysis of English Communication Modalities
Modalities of communication for human beings are gradually increasing in number with the advent of new forms of technology. Many human beings can readily transition between these different forms of communication with little or no effort, which brings about the question: How similar are these different communication modalities? To understand technology$\text{'}$s influence on English communication, four different corpora were analyzed and compared: Writing from Books using the 1-grams database from the Google Books project, Twitter, IRC Chat, and transcribed Talking. Multi-word confusion matrices revealed that Talking has the most similarity when compared to the other modes of communication, while 1-grams were the least similar form of communication analyzed. Based on the analysis of word usage, word usage frequency distributions, and word class usage, among other things, Talking is also the most similar to Twitter and IRC Chat. This suggests that communicating using Twitter and IRC Chat evolved from Talking rather than Writing. When we communicate online, even though we are writing, we do not Tweet or Chat how we write books; we Tweet and Chat how we Speak. Nonfiction and Fiction writing were clearly differentiable from our analysis with Twitter and Chat being much more similar to Fiction than Nonfiction writing. These hypotheses were then tested using author and journalists Cory Doctorow. Mr. Doctorow$\text{'}$s Writing, Twitter usage, and Talking were all found to have very similar vocabulary usage patterns as the amalgamized populations, as long as the writing was Fiction. However, Mr. Doctorow$\text{'}$s Nonfiction writing is different from 1-grams and other collected Nonfiction writings. This data could perhaps be used to create more entertaining works of Nonfiction.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs
Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of…
Abusive LanguageBIG-bench Machine LearningClassificationDialogue Generation+8Constructive Interaction for Talking about Interesting Topics
The paper discusses mechanisms for topic management in conversations, concentrating on interactions where the interlocutors react to each other's presentation of new information and construct a shared context in which to…
ManagementSpeech RecognitionSpeech SynthesisWorld KnowledgeNegativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication
Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens m…
Sentiment AnalysisIncorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior o…
General ClassificationUVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning
With the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others. We have attempted to detect such tweets and comments that are malicious in intent, which …
BIG-bench Machine Learning