東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Linguistic and Emotion-Based Identif...

Bernardes, Vitor Sexto.

Linked to FindBook

Google Book

Amazon

博客來

Linguistic and Emotion-Based Identification of Tweets with Fake News: A Case Study.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Linguistic and Emotion-Based Identification of Tweets with Fake News: A Case Study./
Author:	Bernardes, Vitor Sexto.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	101 p.
Notes:	Source: Masters Abstracts International, Volume: 84-01.
Contained By:	Masters Abstracts International84-01.
Subject:	Sentiment analysis. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29139235
ISBN:	9798835535798

Linguistic and Emotion-Based Identification of Tweets with Fake News: A Case Study.
Bernardes, Vitor Sexto.

Linguistic and Emotion-Based Identification of Tweets with Fake News: A Case Study. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 101 p.

Source: Masters Abstracts International, Volume: 84-01.

Thesis (M.Sc.)--Universidade do Porto (Portugal), 2021.

Since the popularization of the term in 2016, we have observed a proliferation of so called "fake news" content, assisted by the widespread use of social media platforms. The dissemination of fake content brings with it serious political, economical, and health-related real-world impacts, which makes it imperative to find ways to mitigate this problem. In this dissertation we propose a machine learning-based approach to tackle it by automatically identifying tweets associated with questionable content. To that end, we employ a practical approach with a case study using newly collected data from Twitter related to the 2020 US presidential election. In order to create a sizable annotated data set, we use an automatic labeling process based on the factual reporting level of links contained in tweets, as classified by human experts, resulting in a labeled data set containing 150 thousand tweets representative of the real-world scenario of fake news distribution in social media. We derive relevant features from that data, based on a combination of text content representation, linguistic attributes, user profile, and post metadata. We compare different variations of these features and identify the most applicable methods to the problem at hand, with an approach generally based on data obtained from an individual tweet, enabling classification as soon as a tweet is posted, therefore helping to prevent the harm inevitably caused by fake news diffusion. We additionally demonstrate the specific contribution of features derived from named entity and emotion recognition techniques, including a novel approach using sequences of prevalent emotions in sentences, analogous to n-grams. We conclude the dissertation by evaluating and comparing the performance of several machine learning models on a number of test sets, created with different contexts and time periods, and show they are applicable to addressing the issue of fake news dissemination.

ISBN: 9798835535798Subjects--Topical Terms:

3266790
Sentiment analysis.

Linguistic and Emotion-Based Identification of Tweets with Fake News: A Case Study.
LDR:05238nmm a2200337 4500 001 2402352
005 20241028051809.5
006 m o d
007 cr#unu||||||||
008 251215s2021 ||||||||||||||||| ||eng d
020 $a 9798835535798
035 $a (MiAaPQ)AAI29139235
035 $a (MiAaPQ)Portugal10216139434
035 $a AAI29139235
035 $a 2402352
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bernardes, Vitor Sexto. $3 3772580
245 1 0 $a Linguistic and Emotion-Based Identification of Tweets with Fake News: A Case Study.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 101 p.
500 $a Source: Masters Abstracts International, Volume: 84-01.
500 $a Advisor: Figueira, Alvaro.
502 $a Thesis (M.Sc.)--Universidade do Porto (Portugal), 2021.
520 $a Since the popularization of the term in 2016, we have observed a proliferation of so called "fake news" content, assisted by the widespread use of social media platforms. The dissemination of fake content brings with it serious political, economical, and health-related real-world impacts, which makes it imperative to find ways to mitigate this problem. In this dissertation we propose a machine learning-based approach to tackle it by automatically identifying tweets associated with questionable content. To that end, we employ a practical approach with a case study using newly collected data from Twitter related to the 2020 US presidential election. In order to create a sizable annotated data set, we use an automatic labeling process based on the factual reporting level of links contained in tweets, as classified by human experts, resulting in a labeled data set containing 150 thousand tweets representative of the real-world scenario of fake news distribution in social media. We derive relevant features from that data, based on a combination of text content representation, linguistic attributes, user profile, and post metadata. We compare different variations of these features and identify the most applicable methods to the problem at hand, with an approach generally based on data obtained from an individual tweet, enabling classification as soon as a tweet is posted, therefore helping to prevent the harm inevitably caused by fake news diffusion. We additionally demonstrate the specific contribution of features derived from named entity and emotion recognition techniques, including a novel approach using sequences of prevalent emotions in sentences, analogous to n-grams. We conclude the dissertation by evaluating and comparing the performance of several machine learning models on a number of test sets, created with different contexts and time periods, and show they are applicable to addressing the issue of fake news dissemination.
520 $a Desde 2016, com a popularizacao da expressao, temos observado uma proliferacao das chamadas "fake news" (termo em ingles para "noticias falsas"), que tem sido exponenciada pelo uso das plataformas de redes sociais. A disseminacao de conteudos falsos traz consigo graves impactos reais, seja no campo da politica, da economia ou da saude publica, o que torna essencial encontrar maneiras de atenuar esse problema. Nesta dissertacao, propomos uma abordagem para enfrenta-lo com base em aprendizagem computacional, por meio da identificacao automatizada de tweets associados a conteudos questionaveis. Adotamos uma abordagem pratica, realizando um caso de estudo com dados recolhidos do Twitter exclusivamente para esse fim, relacionados a eleicao presidencial norte-americana de 2020. Para obter um conjunto de dados de tamanho consideravel, empregamos um processo automatizado de etiquetagem baseado no grau de reportagem factual de links presentes em tweets, conforme avaliacao de especialistas. Esse processo resultou em um conjunto de dados etiquetado com 150 mil tweets que e representativo da distribuicao real das fake news em redes sociais. A partir desses dados, criamos atributos relevantes, com base em uma combinacao de representacao de texto, propriedades linguisticas, no perfil do utilizador e em metadados das publicacoes. Comparamos variacoes desses atributos e identificamos os metodos mais adequados para enfrentar o problema em questao, com uma abordagem baseada em dados que podem ser obtidos a partir de cada tweet, o que possibilita que ele seja classificado assim que e publicado, o que por sua vez pode ajudar a evitar o dano que e inevitavelmente causado pela divulgacao de fake news. Alem disso, demonstramos a contribuicao especifica de atributos derivados de tecnicas de reconhecimento de entidades nomeadas e de emocoes, com uma nova abordagem que utiliza sequencias de emocoes dominantes em frases, analogas a n-gramas. Concluimos a dissertacao com a avaliacao e a comparacao do desempenho de diversos modelos de aprendizagem computacional em uma serie de conjuntos de teste, criados em diferentes contextos e periodos, com os quais demonstramos a adequacao de modelos para enfrentar o problema da disseminacao das fake news.
590 $a School code: 5896.
650 4 $a Sentiment analysis. $2 lcstt $3 3266790
650 4 $a Emotions. $3 524569
650 4 $a Web studies. $3 2122754
690 $a 0646
710 2 $a Universidade do Porto (Portugal). $3 3348738
773 0 $t Masters Abstracts International $g 84-01.
790 $a 5896
791 $a M.Sc.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29139235