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2021 ◽  
Vol 13 (3) ◽  
pp. 435-449 ◽  
Author(s):  
Temple Uwalaka ◽  
Bigman Nwala ◽  
Amadi Confidence Chinedu

This study investigates the impact of social media ‘fake news’ and fake cures headlines on how Netizens viewed and responded to the COVID-19 pandemic in Nigeria. Using data from an online survey (N=254), this study reveals that social media was overwhelmingly the most used type of media for news consumption generally, and the most important source of news about the pandemic. Data further reveal that the impact of extensive exposure to fake news headlines about the pandemic was dangerous and could have a deleterious impact. Crucially, this study finds that recalling and believing fake news headlines and using social media as the main source of news, significantly decreases the likelihood of believing credible and real news stories. Finally, this study offers theoretical and empirical background to frame the debate about factors that influence the believability of fake news headlines by contributing and extending the theorization of the amplification hypothesis.


2021 ◽  
Author(s):  
Louise Archer ◽  
Claire Standley ◽  
Péter Molnár

As SARS-CoV-2 has swept the planet, intermittent “lockdowns” have become a regular feature to control transmission. References to so-called recurring “waves” of infections remain pervasive among news headlines, political messaging, and public health sources. Here, we consider the power of analogies as a tool for facilitating effective understanding of biological processes by reviewing the successes and limitations of various analogies in the context of the COVID-19 pandemic. We also consider how, when analogies fall short, their ability to persuade can mislead public opinion and behaviour, even if unintentionally. While waves can be effective in conveying patterns of disease outbreak retrospectively, we suggest that process-based analogies might be more effective communication tools, given that they are easily mapped to underlying epidemiological concepts and can be extended to include more complex (e.g., spatial) dynamics. Though no single analogy perfectly captures disease dynamics, fire is particularly suitable for visualizing the epidemiological models that are used to understand disease trajectories, underscoring the importance of and reasoning behind control strategies, and, above all, conveying a sense of urgency to galvanise collective action.


Author(s):  
Jonathan Readshaw ◽  
Stefano Giani

AbstractThis work presents a convolutional neural network for the prediction of next-day stock fluctuations using company-specific news headlines. Experiments to evaluate model performance using various configurations of word embeddings and convolutional filter widths are reported. The total number of convolutional filters used is far fewer than is common, reducing the dimensionality of the task without loss of accuracy. Furthermore, multiple hidden layers with decreasing dimensionality are employed. A classification accuracy of 61.7% is achieved using pre-learned embeddings, that are fine-tuned during training to represent the specific context of this task. Multiple filter widths are also implemented to detect different length phrases that are key for classification. Trading simulations are conducted using the presented classification results. Initial investments are more than tripled over an 838-day testing period using the optimal classification configuration and a simple trading strategy. Two novel methods are presented to reduce the risk of the trading simulations. Adjustment of the sigmoid class threshold and re-labelling headlines using multiple classes form the basis of these methods. A combination of these approaches is found to be more than double the Average Trade Profit achieved during baseline simulations.


Author(s):  
Sofia Baliño Rios

The Central Park jogger case has returned to news headlines with the 2019 Netflix mini-series When They See Us, a dramatised account of the original trials. It has reignited debate over the injustices faced by the Black community in the United States, and led to lawsuits and job resignations on the part of former police investigators and prosecutors. Since the case’s inception, issues of race, media reporting, economics, and the identity of New York City have influenced the trial and its aftermath and have inspired documentaries, books, and the landmark 1990 essay “Sentimental Journeys” by Joan Didion. In this article, I argue that the creators of two of these works, by testing the boundaries of narrative, demonstrate that the case was inexorably tainted by a pervasive feeling of social precarity and racial prejudice which cost five young men several years of their lives, and offer a productive line of enquiry for acknowledging such factors and their influence, if not resolving them.


Author(s):  
Padmanayana ◽  
Varsha ◽  
Bhavya K

Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.


2021 ◽  
Vol 11 (1) ◽  
pp. 124
Author(s):  
Nadya Inda Syartanti

This study aims to reveal the construction of the news about the immoral video case that happened to Indonesia’s celebrity (GA) in various online news media through Norman Fairclough's critical discourse analysis approach. Data sources are taken from various online news media, namely detik.com, kompas.com, liputan6.com, and tribunnews.com in the range of publications from November 2020 to January 2021 with the research subject in the form of news headlines related to GA’s immoral video case. With three dimensions of discourse from Norman Fairclough's critical discourse analysis, the data analysis was carried out by the three stages: the descriptive analysis stage, the interpretation analysis stage, and the explanation analysis stage. The results found that through the microstructural dimension, the eight news headlines used language tools by 1) choosing vocabulary that was focused on the various phrase of video X, and 2) grammatical units which were dominated by clauses, 3) syntactic functions which were dominated by information as topicalization of discourse, and 4) a form of news that emphasizes the affirmation or clarification of GA’s immoral video case. Then, through the mesostructure dimension, the four online mass media have different characteristics and characters in their delivery of news, especially news about the GA’s immoral video case, but are still presented accurately and objectively so that the news content can be conveyed to readers. Finally, through the macrostructural dimension, with the reporting of immoral video cases, GA received a negative image in the eyes of the Indonesian people, because she was considered to be contrary to eastern culture. This negative image is formed because of the news using vulgar, tendentious, and transparent language to reflect press freedom that must be upheld, even though in composing news, sometimes using sarcastic language and making comparisons in it to attract readers' interest


2021 ◽  
Vol 9 (1) ◽  
pp. 319-334
Author(s):  
Mauro Machado do Prado ◽  
Ana Paula de Castro Neves ◽  
Nathália Machado Cardoso Dardeau de Albuquerque

O presente trabalho consiste em um estudo qualitativo das representações sociais de imigrantes venezuelanas na América do Sul no período de 2016 a 2019, a partir de manchetes de notícias divulgadas em jornais digitais brasileiros. O objetivo é verificar a ocorrência ou não de veiculações que constituam de forma explícita ou implícita uma violação à dignidade e aos direitos dessas mulheres, ao fomentar ou incitar a xenofobia e a violência de gênero na sociedade através de palavras, frases ou expressões capazes de provocar um aniquilamento simbólico. Para tanto, realizou-se um estudo bibliográfico e documental acerca das vulnerabilidades sociais presentes nos processos imigratórios contemporâneos, que foi consubstanciado com a análise de conteúdo (BARDIN, 2009), em abordagem qualitativa, de manchetes publicadas em jornais digitais brasileiros. A partir da análise realizada, foi possível inferir que estes veículos de comunicação vêm frequentemente descrevendo a migração venezuelana como um problema, mas em conotação negativa, sem o cuidado de descrição do contexto de forma mais clara e abrangente da questão a ser noticiada.   Xenofobia y violencia de género: un análisis de los titulares de las mujeres venezolanas en el periodismo web brasileño El presente trabajo consiste en un estudio cualitativo de las representaciones sociales de los inmigrantes venezolanos en América del Sur en el período de 2016 a 2019, a partir de titulares de noticias publicados en periódicos digitales brasileños. El objetivo es verificar la ocurrencia o no de colocaciones que constituyan explícita o implícitamente una violación a la dignidad y derechos de estas mujeres, al promover o incitar la xenofobia y la violencia de género en la sociedad a través de palabras, frases o expresiones capaces de provocar una aniquilación simbólica. Para ello, se realizó un estudio bibliográfico y documental sobre las vulnerabilidades sociales presentes en los procesos migratorios contemporáneos, el cual fue fundamentado con análisis de contenido (BARDIN, 2009), en un enfoque cualitativo, de titulares publicados en diarios digitales brasileños. Del análisis realizado, se pudo inferir que estos medios de comunicación han venido describiendo muchas veces la migración venezolana como un problema, pero en una connotación negativa, sin preocuparse por describir de manera más clara y completa el contexto del tema a reportar. Palabras clave: Derechos humanos de la mujer. La violencia de género. Xenofobia. Periodismo web.   Xenophobia and gender violence: an analysis of headings broadcasted in brazilian webjornalism on venezuelan women The present work consists of a qualitative study of the social representations of Venezuelan immigrants in South America in the period from 2016 to 2019, based on news headlines published in Brazilian digital newspapers. The objective is to verify the occurrence or not of placements that explicitly or implicitly constitute a violation of the dignity and rights of these women, by promoting or inciting xenophobia and gender violence in society through words, phrases or expressions capable of provoking a symbolic annihilation. To this end, a bibliographic and documentary study was carried out on the social vulnerabilities present in contemporary immigration processes, which was substantiated with content analysis (BARDIN, 2009), in a qualitative approach, of headlines published in Brazilian digital newspapers. From the analysis carried out, it was possible to infer that these media outlets have often been describing Venezuelan migration as a problem, but in a negative connotation, without taking care to describe the context more clearly and comprehensively of the issue to be reported. Keywords: Women’s human rights. Gender-based violence. Xenophobia. Webjournalism.


Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 250
Author(s):  
Kittisak Prachyachuwong ◽  
Peerapon Vateekul

A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To obtain better news sentiment, we have divided the headlines into industry-specific indexes (also called “sectors”) to reflect the movement of securities of the same fundamental. The proposed method consists of Long Short-Term Memory Network (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) architectures to predict daily stock market activity. We have evaluated model performance by considering predictive accuracy and the returns obtained from the simulation of buying and selling. The experimental results demonstrate that enhancing both numerical and textual information of each sector can improve prediction performance and outperform all baselines.


PLoS Biology ◽  
2021 ◽  
Vol 19 (6) ◽  
pp. e3001260
Author(s):  
Marcia Triunfol ◽  
Fabio C. Gouveia

There is increasing scrutiny around how science is communicated to the public. For instance, a Twitter account @justsaysinmice (with 70.4K followers in January 2021) was created to call attention to news headlines that omit that mice, not humans, are the ones for whom the study findings apply. This is the case of many headlines reporting on Alzheimer disease (AD) research. AD is characterized by a degeneration of the human brain, loss of cognition, and behavioral changes, for which no treatment is available. Around 200 rodent models have been developed to study AD, even though AD is an exclusively human condition that does not occur naturally in other species and appears impervious to reproduction in artificial animal models, an information not always disclosed. It is not known what prompts writers of news stories to either omit or acknowledge, in the story’s headlines, that the study was done in mice and not in humans. Here, we raised the hypothesis that how science is reported by scientists plays a role on the news reporting. To test this hypothesis, we investigated whether an association exists between articles’ titles and news’ headlines regarding the omission, or not, of mice. To this end, we analyzed a sample of 623 open-access scientific papers indexed in PubMed in 2018 and 2019 that used mice either as models or as the biological source for experimental studies in AD research. We found a significant association (p < 0.01) between articles’ titles and news stories’ headlines, revealing that when authors omit the species in the paper’s title, writers of news stories tend to follow suit. We also found that papers not mentioning mice in their titles are more newsworthy and significantly more tweeted than papers that do. Our study shows that science reporting may affect media reporting and asks for changes in the way we report about findings obtained with animal models used to study human diseases.


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