Editors Choice – Stock Market Prediction Based on Technical-Deviation-ROC Indicators Using Stock and Feeds Data

Author(s):Deepika N. and P. Victer Paul*

Volume 15, Issue 3, 2022

Published on: 31 August, 2020

Article ID: e180322185408

Pages: 9

DOI: 10.2174/2666255813999200831120847


Background: The attempt of this research is to propose a novel approach for the efficient prediction of stock prices. The scope of this research extends by including the feature of sentiment analysis using the emotions and opinions carried by social media platforms. The research also analyzes the impact of social media, feeds data and Technical indicators on stock prices for the design of the prediction model.

Objectives: The goal of this research is to analyze and compare the models to predict stock trends by adjusting the feature set.

Methods: The basic technical and new momentum volatility indicators are calculated for the benchmark index values of the stock. The text summarization was applied on collected day-wise tweets for a particular company and then sentiment analysis was performed to get the sentiment value. All these collected features were integrated to form the final dataset and accuracy comparisons were made by experimenting with the algorithms- Support vector machine (SVM), Backpropogation and Long short-term memory (LSTM).

Results: The execution is carried out for each algorithm with 30 epochs. It is observed that the SVM exhibits 2.78%, Backpropogation exhibits 5.02% and LSTM exhibits 10.30 % enhanced performance than the prediction model designed using basic technical indicators. Moreover, along with human sentiment, the SVM provides 5.48%, Backpropogation 5.28% and LSTM 0.07% better accuracy. The standard deviation results are for SVM 1.59, for back propagation 2.46, and LSTM 0.19.

Conclusion: The experimental results show that the standard deviation of LSTM is less than the SVM and back propagation algorithms. Hence, obtaining steady accuracy is highly possible with LSTM. Read now: https://bit.ly/3vDiZ94


Using Digital and Social Media Metrics to Develop Mental Health Approaches for Youth

Author(s): Christina Carew, Stan Kutcher, Yifeng Wei, Alan McLuckie.





Objective: The objective of this project was to investigate the online behaviors of adolescent and adult populations with respect to mental health information seeking, and identify differences in approaches within age groups and geographical location.

Method: This content analysis approach identified and mapped patterns in online conversations. The search data was able to quantify who was looking for teen mental health information, where they were looking, and what they were looking for. Additional analysis included the preferred format of information presentation and how mental health searches varied over time.

Results: The results of the analysis revealed that between 2006 (baseline) to 2010, a 200% increase in online activity regarding mental health was identified. Adults were most likely to ignite (initiate) conversations online about depression, followed by: anxiety, doctors, suicide, treatments, and OCD. For teens, depression was also the most ignited topic area, followed by: anxiety, alcohol, suicide, sexting and marijuana. While adults were often seeking information about the disorders and treatment options, teens tended to discuss concerns through the use of personal stories.

Conclusion: This research provides insight into how digital and social media can be used to engage both youth and adult discussions about mental health. We report substantive audience driven differences that can inform the development of targeted mental health knowledge translation methods and activities. A broader understanding of the key mental health topics of interest was garnered, in addition to how online use varied between audiences. These results have several implications for mental health knowledge translation including tactics to connect with various stakeholders.


Read more here: http://www.eurekaselect.com/123250 

Twitter Scholars: How Science Goes Viral

Scientists are beginning to use social media to measure the impact of their work — but they’re still in the process of figuring out what online popularity means.

In times past, counting up scholarly citations– i.e. how often other academics were using your work for their own research – was one of the only ways to know how widely read and appreciated a piece of research had been.

But today, a number of journals publish more modern alternative metrics, or altmetrics, such as how many times articles have been tweeted, shared on Facebook, downloaded, or written up in news reports. And institutions and scientists can track responses to their work using services offered by new nonprofits and companies.


But what does it mean to have a paper go viral on social media? And what’s more important: tweet-ability or the traditional citation from the scientific community?

study analyzing Twitter links to biomedical articles, which was published last month in the Journal of the American Society for Information Science and Technology, indicates that popularity on Twitter is probably not a reliable measure of scholarly esteem. Twitter mentions show a low correlation with citations, so a tweetable paper isn’t necessarily a well respected one.

“It’s one of the largest studies that looked at the connection between scientific articles and their [mentions] on social media,” said Emily Darling, a David H Smith Conservation Research Fellow at the University of North Carolina who studies both coral reefs and how scholars use Twitter. “It was interesting that there’s no connection to scholarly impact.”

I was surprised that the correlation was so low,” said Euan Adie, cofounder ofAltmetric, a London-based company that tracks Internet response to scholarly works. The study reinforces a point Adie and his Altmetrics colleagues often make: high levels of online engagement do not necessarily say anything about an article’s scholarly quality.

Lead author Stefanie Haustein, a researcher at the University of Montreal, and colleagues used data from Altmetric to track tweets of links to papers published between 2010 and 2012. The study encompassed 1.4 million studies indexed on PubMed, a site that catalogs biomedical articles, and Web of Science, a site that tracks scholarly citations. Haustein’s team found that less than 10 percent of these articles were ever tweeted at all. They used only papers published in 2011 to check correlations between tweets and citations.

“Social media was never meant to replace traditional statistics like journal impact factors or article citations,” Darling writes in an article published last week on The Conversation that responds to Haustein’s study. Rather,  “Twitter gives you connections beyond the ivory tower that you don’t normally have,” she said. As a conservationist, for instance, she hopes to spread her research to policymakers and resource managers, so they can take actions based on it. And she wants to inform the public.

But Haustein says that more work is needed to distinguish between the various reasons an article is tweeted—whether to spread important information or for fun. For instance, Altmetric last week released a list of the top 100 papers that received the most online attention in 2013, including tweets, Facebook posts, news stories, blog posts, and more.

Some papers were clearly shared because they were of vital interest to the public. A study related to the Fukushima nuclear disaster in Japan topped the list. It was an open-access paper that chronicled the levels of radioactive cesium found in freshwater fish around Japan, and it was tweeted largely by members of the public, with the highest representation in Japan.

[Courtesy of: http://www.theconnectivist.com/2013/12/twitter-scholars-how-science-goes-viral/

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