scholarly journals Social Media, News Media and the Stock Market

2016 ◽  
Author(s):  
Peiran Jiao ◽  
Ansgar Walther
Keyword(s):  
Social Media ◽  
Stock Market ◽  
News Media

scholarly journals Social media, news media and the stock market

2020 ◽  
Vol 176 ◽  
pp. 63-90
Author(s):  
Peiran Jiao ◽  
André Veiga ◽  
Ansgar Walther
Keyword(s):  
Social Media ◽  
Stock Market ◽  
News Media

scholarly journals A Systematic Study on Sentiment Analysis based on Text Mining and Deep learning for Predictions in Stock Market Trends Through Social and News Media Data

2021 ◽  
Vol 9 (10) ◽  
pp. 1589-1593
Author(s):  
Suvigya Jain
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Stock Market ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
News Media ◽  
Market Sentiment ◽  
Market Trends

Abstract: Stock Market has always been one of the most active fields of research, many companies and organizations have focused their research in trying to find better ways to predict market trends. The stock market has been the instrument to measure the performance of a company and many have tried to develop methods that reduce risk for the investors. Since, the implementation of concepts like Deep Learning and Natural Language Processing has been made possible due to modern computing there has been a revolution in forecasting market trends. Also, the democratization of knowledge related to companies made possible due to the internet has provided the stake holders a means to learn about assets they choose to invest in through news media and social media also stock trading has become easier due to apps like robin hood etc. Every company now a days has some kind of social media presence or is usually reported by news media. This presence can lead to the growth of the companies by creating positive sentiment and also many losses by creating negative sentiments due to some public events. Our goal in this paper is to study the influence of news media and social media on market trends using sentiment analysis. Keywords: Deep Learning, Natural Language Processing, Stock Market, Sentiment analysis

How Social Media Impacts Mass Media News Viewership in the Stock Market

2019 ◽  
Author(s):  
Jie Ren ◽  
Hang Dong ◽  
Gaurav Sabnis ◽  
Jeffrey V. Nickerson
Keyword(s):  
Social Media ◽  
Mass Media ◽  
Stock Market

scholarly journals Introduction to special issue about election reporting: Why journalism (still) matters

Journalism ◽  
2019 ◽  
Vol 20 (8) ◽  
pp. 985-993 ◽  
Author(s):  
Stephen Cushion ◽  
Daniel Jackson
Keyword(s):  
Social Media ◽  
Political Parties ◽  
Political Communication ◽  
News Media ◽  
Election Campaigns ◽  
Special Issue ◽  
Technological Advances ◽  
Campaign Coverage ◽  
Election Coverage ◽  
Social Media Platforms

This introduction unpacks the eight articles that make up this Journalism special issue about election reporting. Taken together, the articles ask: How has election reporting evolved over the last century across different media? Has the relationship between journalists and candidates changed in the digital age of campaigning? How do contemporary news values influence campaign coverage? Which voices – politicians, say or journalists – are most prominent? How far do citizens inform election coverage? How is public opinion articulated in the age of social media? Are sites such as Twitter developing new and distinctive election agendas? In what ways does social media interact with legacy media? How well have scholars researched and theorised election reporting cross-nationally? How can research agendas be enhanced? Overall, we argue this Special Issue demonstrates the continued strength of news media during election campaigns. This is in spite of social media platforms increasingly disrupting and recasting the agenda setting power of legacy media, not least by political parties and candidates who are relying more heavily on sites such as Facebook, Instagram and Twitter to campaign. But while debates in recent years have centred on the technological advances in political communication and the associated role of social media platforms during election campaigns (e.g. microtargeting voters, spreading disinformation/misinformation and allowing candidates to bypass media to campaign), our collection of studies signal the enduring influence professional journalists play in selecting and framing of news. Put more simply, how elections are reported still profoundly matters in spite of political parties’ and candidates’ more sophisticated use of digital campaigning.

Political Knowledge and Misinformation in the Era of Social Media: Evidence From the 2015 UK Election

2020 ◽  
pp. 1-21
Author(s):  
Kevin Munger ◽  
Patrick J. Egan ◽  
Jonathan Nagler ◽  
Jonathan Ronen ◽  
Joshua Tucker
Keyword(s):  
Social Media ◽  
Political Parties ◽  
News Media ◽  
Political Knowledge ◽  
Public Affairs ◽  
Political Information ◽  
The Political ◽  
Election Campaign ◽  
Social Media Platform ◽  
Media Platform

Abstract Does social media educate voters, or mislead them? This study measures changes in political knowledge among a panel of voters surveyed during the 2015 UK general election campaign while monitoring the political information to which they were exposed on the Twitter social media platform. The study's panel design permits identification of the effect of information exposure on changes in political knowledge. Twitter use led to higher levels of knowledge about politics and public affairs, as information from news media improved knowledge of politically relevant facts, and messages sent by political parties increased knowledge of party platforms. But in a troubling demonstration of campaigns' ability to manipulate knowledge, messages from the parties also shifted voters' assessments of the economy and immigration in directions favorable to the parties' platforms, leaving some voters with beliefs further from the truth at the end of the campaign than they were at its beginning.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Source Trust and COVID-19 Information Sharing: The Mediating Roles of Emotions and Beliefs About Sharing

2020 ◽  
pp. 109019812098476
Author(s):  
Linqi Lu ◽  
Jiawei Liu ◽  
Y. Connie Yuan ◽  
Kelli S. Burns ◽  
Enze Lu ◽  
...  
Keyword(s):  
Social Media ◽  
Information Sharing ◽  
Health Information ◽  
News Media ◽  
Health Professionals ◽  
Government Agencies ◽  
Specific Information ◽  
Academic Institutions ◽  
Source Trust ◽  
Health Information Sharing

Health information sharing has become especially important during the COVID-19 (coronavirus disease 2019) pandemic because people need to learn about the disease and then act accordingly. This study examines the perceived trust of different COVID-19 information sources (health professionals, academic institutions, government agencies, news media, social media, family, and friends) and sharing of COVID-19 information in China. Specifically, it investigates how beliefs about sharing and emotions mediate the effects of perceived source trust on source-specific information sharing intentions. Results suggest that health professionals, academic institutions, and government agencies are trusted sources of information and that people share information from these sources because they think doing so will increase disease awareness and promote disease prevention. People may also choose to share COVID-19 information from news media, social media, and family as they cope with anxiety, anger, and fear. Taken together, a better understanding of the distinct psychological mechanisms underlying health information sharing from different sources can help contribute to more effective sharing of information about COVID-19 prevention and to manage negative emotion contagion during the pandemic.

scholarly journals A Multi-platform Approach to Monitoring Negative Dominance for COVID-19 Vaccine-Related Information Online

2021 ◽  
pp. 1-24
Author(s):  
Paola Pascual-Ferrá ◽  
Neil Alperstein ◽  
Daniel J. Barnett
Keyword(s):  
Social Media ◽  
Adverse Events ◽  
News Media ◽  
Search Behavior ◽  
Social Media Data ◽  
Future Studies ◽  
Related Information ◽  
Youtube Videos ◽  
Negative Dominance ◽  
Media Data

Abstract Objective The aim of this study was to test the appearance of negative dominance in COVID-19 vaccine-related information and activity online. We hypothesized that if negative dominance appeared, it would be a reflection of peaks in adverse events related to the vaccine, that negative content would attract more engagement on social media than other vaccine-related posts, and posts referencing adverse events related to COVID-19 vaccination would have a higher average toxicity score. Methods We collected data using Google Trends for search behavior, CrowdTangle for social media data, and Media Cloud for media stories, and compared them against the dates of key adverse events related to COVID-19. We used Communalytic to analyze the toxicity of social media posts by platform and topic. Results While our first hypothesis was partially supported, with peaks in search behavior for image and YouTube videos driven by adverse events, we did not find negative dominance in other types of searches or patterns of attention by news media or on social media. Conclusion We did not find evidence in our data to prove the negative dominance of adverse events related to COVID-19 vaccination on social media. Future studies should corroborate these findings and, if consistent, focus on explaining why this may be the case.

New Indian online rules risk state overreach and abuse

2021 ◽  
Keyword(s):  
Social Media ◽  
News Media ◽  
Public Discourse ◽  
Hate Speech ◽  
Central Government ◽  
Service Providers ◽  
Online News ◽  
Content Type ◽  
Digital Service ◽  
The Government

Significance The new rules follow a stand-off between Twitter and the central government last month over some posts and accounts. The government has used this stand-off as an opportunity not only to tighten rules governing social media, including Twitter, WhatsApp, Facebook and LinkedIn, but also those for other digital service providers including news publishers and entertainment streaming companies. Impacts Government moves against dominant social media platforms will boost the appeal of smaller platforms with light or no content moderation. Hate speech and harmful disinformation are especially hard to control and curb on smaller platforms. The new rules will have a chilling effect on online public discourse, increasing self-censorship (at the very least). Government action against online news media would undercut fundamental democratic freedoms and the right to dissent. Since US-based companies dominate key segments of the Indian digital market, India’s restrictive rules could mar India-US ties.

Sign in / Sign up

Export Citation Format

Share Document