News Article

Accurate forecasting of upcoming trends in the capital markets is extremely important for algorithmic trading and investment management. Before making a trading decision, investors estimate the probability that a certain news item will influence the market based on the available information. Speculation among traders is often caused by the release of a breaking news article and results in price movements. Publications of news articles influence the market state that makes them a powerful source of data in financial forecasting. Recently, researchers have developed trend and price prediction models based on information extracted from news articles. However, to date no previous research that investigates the advantages of using news articles with different levels of relevance to the target stock has been conducted. This research study uses the multiple kernel learning technique to effectively combine information extracted from stock-specific and sub-industry-specific news articles for prediction of an upcoming price movement. News articles are divided into these two categories based on their relevance to a targeted stock and analyzed by separate kernels. The experimental results show that utilizing two categories of news improves the prediction accuracy in comparison with methods based on a single news category.

Using human intelligence to identify news articles' political stances is common in research and practical applications. But human judgement can be biased and prone to errors stemming from the comprehension of tasks and political alignment. This paper proposes a relative rating method based on news articles' stances relative to raters' own stances to avoid comprehension inconsistency and to control for human bias in crowdsourced stance detection of news articles. We also show how to use the relative ratings to construct a measure for raters' stances on a political topic and to identify raters whose ratings are of higher quality than others. We implement our proposed methods in an online experiment that recruits Amazon Mechanical Turk users as raters for news articles on Gun Control. Using the data from the experiment, we find evidence that raters' own stances on Gun Control significantly impact ratings of related news articles, both at the individual levels and at the aggregate levels. We also present evidence that our relative-rating-based stance measure captures more information about raters' actual stances than their self-reported stance does.

We live in an era of information explosion where media for information communication and information sharing have proliferated due to the onset of a digital age. One such important source and medium of consolidated, comprehensive and reliable information, which has persisted through centuries, is news articles. News articles have been considered as a primary source of information, ranging from local to global scope, and encompassing a wide array of domains, by most of the human population. However, as the technology for dissemination of information attracts innovations, it becomes equally important that the consumption and utilization of this information becomes smart as well. If processed properly and treated in an intelligent manner, the information contained in news articles can reveal useful insights and hidden patterns that might prove to be of critical importance in certain decision making pertaining to the topics and domains in question. The paper puts forward a novel framework created by combining Natural Language Processing methodologies like Sentiment Analysis and Information Retrieval, which are performed on top of a Deep Learning based classification with aptly defined classes for generating useful insights from a news article dataset. The paper contains the details regarding the implementation of this system and also a presentation of results on a particular data set of news articles - articles on HIV-AIDS, which have been categorized into meaningful classes and have been processed using aforementioned NLP tasks. Through this paper, a curated dataset of 3,000 news articles related to HIV-AIDS has also been introduced, which will be open sourced so as to inspire further research and experimentation in this specific domain.

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At present, news channels are using social media to disseminate news to a large audience. These news channels try to convey the news in such a way that attracts more user interaction in the form of views, likes, comments, and shares. In online news, one of the important factors in getting higher user interaction is to recommend the top news articles that would attract more number of users to give opinion, especially in the form of comments. When a news article starts getting many comments, it automatically attracts other readers to participate in the discussion. We say that a news article has “high-arousal” content if it can attract more number of comments from users. When a new news article is written it has no user interaction information, such as number of views, likes, shares or comments. In this paper, our aim is to predict news articles which have higher potential to generate high-arousal. In other words, they would attract a large number of users to give opinion in the form of comments. Unlike previous studies, we predict the arousal of news articles prior to their release, which brings the possibility of appropriate decision making to modify the article content or its ranking in audience newsfeed. We generate multiple features from the content of news articles and show that our best set of features can predict the arousal with an accuracy of 81%. We perform our experiments on social media page of CNN news channel, containing four years of data with 33,324 news articles and 226.83 million reactions.

Index to Volme 62, 2018

In this era of fake news and political polarization, it is desirable to have a system to enable users to access balanced news content. Current solutions focus on top down, server based approaches to decide whether a news article is fake or biased, and display only trusted news to the end users. In this paper, we follow a different approach to help the users make informed choices about which news they want to read, making users aware in real time of the bias in news articles they were browsing and recommending news articles from other sources on the same topic with different levels of bias. We use a recent Pew research report to collect news sources that readers with varying political inclinations prefer to read. We then scrape news articles on a variety of topics from these varied news sources. After this, we perform clustering to find similar topics of the articles, as well as calculate a bias score for each article. For a news article the user is currently reading, we display the bias score and also display other articles on the same topic, out of the previously collected articles, from different news sources. This we present to the user. This approach, we hope, would make it possible for users to access more balanced articles on given news topics. We present the implementation details of the system along with some preliminary results on news articles.

With most Internet users now getting news from social media, there is growing concern about how to verify the content that appears on these platforms. Two experiments tested the effects of fact-checking labels (confirmed vs. disputed) by source (peer vs. third-party) on credibility, virality, and information seeking of news posted on social media. Study 1 (N = 312) tested the effects of these labels on memes, and Study 2 (N = 452) tested the same effects on news articles. Results indicate that, although fact-checking labels do not seem to have a beneficial effect on credibility perceptions of individual news posts, their presence does seem to increase judgments of the site’s quality overall. This presents key implications for theory and design in fact-checking and news consumption on social media.

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.

A textual data processing task that involves the automatic extraction of relevant and salient keyphrases from a document that expresses all the important concepts of the document is called keyphrase extraction. Due to technological advancements, the amount of textual information on the Internet is rapidly increasing as a lot of textual information is processed online in various domains such as offices, news portals, or for research purposes. Given the exponential increase of news articles on the Internet, manually searching for similar news articles by reading the entire news content that matches the user’s interests has become a time-consuming and tedious task. Therefore, automatically finding similar news articles can be a significant task in text processing. In this context, keyphrase extraction algorithms can extract information from news articles. However, selecting the most appropriate algorithm is also a problem. Therefore, this study analyzes various supervised and unsupervised keyphrase extraction algorithms, namely KEA, KP-Miner, YAKE, MultipartiteRank, TopicRank, and TeKET, which are used to extract keyphrases from news articles. The extracted keyphrases are used to compute lexical and semantic similarity to find similar news articles. The lexical similarity is calculated using the Cosine and Jaccard similarity techniques. In addition, semantic similarity is calculated using a word embedding technique called Word2Vec in combination with the Cosine similarity measure. The experimental results show that the KP-Miner keyphrase extraction algorithm, together with the Cosine similarity calculation using Word2Vec (Cosine-Word2Vec), outperforms the other combinations of keyphrase extraction algorithms and similarity calculation techniques to find similar news articles. The similar articles identified using KPMiner and the Cosine similarity measure with Word2Vec appear to be relevant to a particular news article and thus show satisfactory performance with a Normalized Discounted Cumulative Gain (NDCG) value of 0.97. This study proposes a method for finding similar news articles that can be used in conjunction with other methods already in use.

BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Ubiquitous access to internet has resulted in more and more people going online to get their daily dose of news. In a 2010 survey conducted by the Pew Project for Excellence in Journalism, 41% of the respondents said they get most of their news online, 10% more than those who said they got most of their news from a newspaper. A lot of socio-technical factors have contributed to this phenomenal rise in adoption of online news in recent years. One of the biggest reasons why people are increasingly reading news online is because it facilitates discussion with peers (Nguyen 2010), offering different viewpoints which aid in forming a rounded personal opinion about the news story. The Pew survey found that 37% of online news users (and 51% of 18-29 year olds) think that commenting on news stories is an important feature to have. A lot of people tend to shape their opinion by reading discussion comments, reflective articles, blogs and even tweets about the news. Hence, an increasing number of people rely on online sources of news – be it news websites or news aggregator services like Digg, Reddit, Google Reader, Flipboard, Pulse etc. The problem with these news websites and aggregators is that the only way people can gather public opinion is by actively searching through the endless stream of comments and feeds, filtering out spam (which is a growing problem) and then reading the relevant posts. A top trending story on Twitter will typically see multiple tweets per second, and keeping up with the rapid flow of incoming tweets is quite cumbersome and cognitively taxing. Hence it becomes increasingly difficult and time consuming for someone who wants to get the pulse of the people affected by a news story. Furthermore, in certain scenarios people might want to look at more fine grained opinions. Currently, there is no elegant way to extract geographic and demographic impact of a news story. What is the public sentiment in Indonesia about the Arab Spring? How did the public opinion about the Wikileaks disclosures change as the story unfolded during the course of a year? It is very difficult and tedious to observe such patterns using the currently available news providers. This work attempts to solve these problems by proposing a news aggregator platform which pulls news stories from various sources and also aggregates public responses, reflections, opinions and sentiments associated with those stories. This data is presented in ways that are easily understandable so readers can make better sense of the stories unfolding across the globe. Such a news aggregator platform that gathers and display public opinion and sentiments about a story, must deal with various challenges – 1. Opinions are very subjective. Different people feel about a story in different ways. With such an enormous amount of diverse opinions and subjectivity, how can we possibly aggregate the responses into something that makes sense as a whole? ________________________________ Acknowledgements: Prof. Yardi, S. for the guidance Sethi, P. (2013). Public opinion aggregation by annotation and tagging of online news stories. iConference 2013 Proceedings (pp. 891-894). doi:10.9776/13453 Copyright is held by the author. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 892 2. There isn’t really a unified web standard for expressing opinion (in textual form). Some people tweet in 140 characters, while others write elaborate blog posts. Some websites employ tags which a reader can use to define and classify their public opinion, while others rely on threaded comments and comment ranking systems. How can a platform be flexible enough to adapt to all these varied standards so that it can extract valuable data from various sources? Perhaps the platform can create a new standard of expression on the web which is flexible and comprehensive enough to be used to express diverse views about every news story in the world. 3. How to filter out spam while extracting public opinion? 4. Once the platform has access to the data it needs, how should it be displayed to the reader in a way that makes sense? What forms of visualizations, illustrations and graphical representations can be employed to give the reader a holistic view of how people feel about a story? 5. How can the platform determine and convey effects of geographical, demographic and temporal variations as the story unfolds? These are just a few out of possibly many issues which must be dealt with. Previous research on similar public opinion aggregation services has greatly focused on natural language processing, data mining and text categorization and clustering. Xiaojun (2010) proposed a framework for crawling the web for comments and applying various data mining algorithms on the data to extract relevant information. Diakopoulos and Shamma (2010) used tweets posted in conjunction with the live presidential debate between Barack Obama and John McCain to gauge public opinion. Brody and Diakopoulos (2011) studied the use of word lengthening to detect sentiment in microblogs. This research proposes a solution – The Opinionated Reader, which relies on sentiment tags and annotations associated with a news story. The essential idea is to create a commenting, discussion and sharing plug-in which can be used by news websites and aggregators as a commenting solution for their news pages. Users wanting to share or comment on a news story through the plug-in are asked to tag the news story with sentiment tags and annotate the story with their reaction (happy/positive or sad/negative). These tags and annotations are stored, aggregated and linked to each news story. A mobile application provides the front-end interface for users to access the news stories and the aggregated sentiment associated with each story. The basic architecture is explained as follows: The Opinionated Reader – Mobile/Tablet App The app fetches news articles from various web sources, based on the interests and preferences configured by the user. In every news article, a portion of the screen real estate is reserved for Opinions which shows graphical visualizations and illustrations of the public opinion surrounding the news story. These visualizations include: A Sentiment Graph indicating the popular tags associated with the story (E.g.: “Shocking”, “Inspiring”, “Amusing” etc.). See Figure 1 for example visualization. A Positivity Graph which plots the level of positivity associated with the story on a time scale from when the news broke. See Figure 2 for example visualization. Figure 1. Example Sentiment Graph Figure 2. Example Positivity Graph iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 893 The user can choose to see these visualizations for a particular time period in the evolution history of the news story, or for a specific country. The app also facilitates people to tag and annotate news articles from within its interface. The Opinionated Reader – Commenting and Sharing Web Plug-in These days, a common way of adding discussion and commenting functionality to news websites is by using 3 rd party services (like DISQUS). The Opinionated Reader is a similar service which can be embedded into the news articles of various news websites to enable commenting and sharing. When someone wishes to comment on an article, the comment is directed through this plug-in, which allows the users to annotate the article with the sentiment tags and reactions along with their comments. The Opinionated Reader saves this information along with the commenter’s location and date of comment (See Figure 3). Figure 3. The Opinionated Reader Web Plug-in 'Add Comment' dialog mockup The Opinionated Reader – Back-end The Back-end maintains a database of news items extracted from RSS feeds of various news websites. Each news article is linked with the sentiment tags and reaction/positivity annotations extracted from the comments and annotations gathered by the commenting plug-in. This data is used by the mobile/tablet app to generate visualizations (Sentiment Graph and Positivity Graph). The back-end also performs data mining on the tags and annotations for geographies and tracks the opinions across time. The back end system responds to queries received from the mobile app with the news story and associated tags and annotations, which are then rendered by the mobile app for the user. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 894 Discussion and Conclusion This design idea is still in a nascent state and has long hours of research, brainstorming, designing and development to go before it can be realized into something tangible. Twitter has grown exponentially in importance as a news source and it would be vastly valuable to integrate Twitter with The Opinionated Reader. Possibilities include use of special hash tags and natural language processing of tweets to extract public sentiment. The current design supports only two reaction annotations – positive and negative. Not every news story fits this annotation paradigm. Further research about human reactions to news stories might unveil interesting insights which would help zero in on a more robust annotation rubric. Lastly, since this service is envisioned to be non-curated and non-moderated, the value served by the app depends on the users themselves. Greater adoption will lead to more annotations and tags, which translates into a more accurate public opinion as presented to the user.

Intertextuality refers to the relationship with which texts are interwoven with each other. It is used to describe the range of ways in which texts make reference to other texts. Recently, mass media and modern technology has become a global issue and started influencing every individual. The information presented in the mass media affects the way people think. Thus, both form and content of mass media are of major importance. News article reporters need to be creative in their language and, at the same time, refer to facts and use common forms of language. Intertextuality is very common among mass media reporters because it is a powerful tool that serves their purposes. The present paper aims at analyzing the reasons which motivates reporters of the news articles on the security situation in Iraq to resort to intertextuality. The period between April and July 1214 is one in which the security situation in Iraq has been in turmoil. Therefore, twelve news articles belonging to the period above have been downloaded from the internet and analyzed carefully. The model adopted in the analysis is that of Bazerman (2004:5) for being a unified comprehensive scheme which covers all the types of techniques presented by other linguists. The analysis has revealed a heavy use of intertextuality in these news articles with particular focus on particular techniques rather than the others for certain reasons that serve the reporters’ purposes.

Abstract. How weather hazards are communicated by the media is important. Which risks are understood, prioritized and acted upon can be influenced by the level of attention they receive. The presented work investigates whether or not the number of weather hazard news articles has increased since 2017, which weather hazards received the most attention in the news articles, and how often climate change was discussed in relation to weather hazards in these news articles. The methods used are advanced searches of Google and the Emergency Disaster Database (EM-DAT) for media articles considering weather hazards – specifically floods, heat waves, wildfires, storms and droughts – between 2017 and 2022. Results suggest that storms are more likely to be reported than any other climate risk, though wildfires generate more news articles per event. Bias in reporting needs to be addressed and is important, because it can exacerbate un-preparedness.

This study examined the information landscape on cervical cancer by employing the extended cancer infodemiology framework and integrated analysis. We analyzed and compared news articles and online platform posts as the two primary sources of health information regarding cancer. Data were collected from BigKinds, a news article database, and blogs and café posts on Naver, which dominates the health information search market in Korea. We analyzed 68,977 documents (news n = 1,043, blog posts n = 56,455, café posts n = 11,479) using traditional manual coding, frequency analysis, and topic modeling to address our research questions. Results show that the key topics differ between news articles and online platform posts on cervical cancer. News covered the “development and clinical trials of treatment” prominently, while recommended hospitals for checkups (blogs) and seeking recommendations for OB/GYN clinics (cafés) were prominent in online platforms. The Cervical Cancer Awareness Month could not influence the frequency or topic of news articles or online platform posts on cervical cancer in Korea. Moreover, the findings show that the dominant engagers were economic daily newspapers (news articles), hospital accounts (blogs), and the cafés on family and childrearing. This study offers empirical evidence of differing pictures across digital platforms. Furthermore, it discusses the possibility that dominant engagers or their interests could have influenced different pictures in the information landscape on cervical cancer.