Multiple News Research Articles

An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology’s reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND.Graphical

Guideline for the Production of Digital Rights Management (DRM)

Multiple news sources over the years have reported on the problematic effects of Digital Rights Management, yet there are no reforms for DRM development, simply removal. The issues are well-known to the public, frequently repeated even when addressed: impact on the software and to the devices that run them. Yet few, if any, have discussed it in recent years, especially with the intent of eliminating the shown issues. This study reviews Digital Rights Management as a general topic, including the various forms it can take, the current laws that affect DRM, and the current public reception and responses. This study describes the different types of DRM in general terms and then lists both positive and negative examples.

Black Swan Theory of Events and the Impact to Hospitality Operators: Literature Review and Proposed Analysis

The Black Swan theory of events is an incident that deviates beyond what is normally expected of a situation and is extremely difficult to predict (Talib, 2007). Black Swan events impact the hospitality industry in many ways. Weather, widespread illness, civil unrest, terrorism, and loss of utilities are out of the immediate control of the industry’s participants but impact their results. Occurrences at the property/corporate level can also impact performance in ways such as: food contaminants impacting restaurants, cruise ships losing power, running a ground, or sinking, experiencing a widespread Nora virus, hotels face fires, strikes, data breaches, boycotts, as well as events such as amusement parks having ride fatalities and animal welfare protests, etc. All of these events are quickly spread by multiple news sources, and consumers are able to search quickly using Google or other search engines to get more details.Previous studies have looked at the aftereffects of exogenous—or self-inflicted— events, and how management teams reacted or did not react and the impact to operating results (Kosova & Enz, 2012). However, there is a paucity of academic analysis of Black Swan events to determine whether there is a way to reduce the impact.

Understanding the Research Challenges in Low-Resource Language and Linking Bilingual News Articles in Multilingual News Archive

The developed world has focused on Web preservation compared to the developing world, especially news preservation for future generations. However, the news published online is volatile because of constant changes in the technologies used to disseminate information and the formats used for publication. News preservation became more complicated and challenging when the archive began to contain articles from low-resourced and morphologically complex languages like Urdu and Arabic, along with English news articles. The digital news story preservation framework is enriched with eighteen sources for Urdu, Arabic, and English news sources. This study presents challenges in low-resource languages (LRLs), research challenges, and details of how the framework is enhanced. In this paper, we introduce a multilingual news archive and discuss the digital news story extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, i.e., English, and low-resource languages, i.e., Urdu and Arabic. LRLs encountered a high error rate during preservation compared to high-resource languages (HRLs), corresponding to 10% and 03%, respectively. The extraction results show that few news sources are not regularly updated and release few new news stories online. LRLs require more detailed study for accurate news content extraction and archiving for future access. LRLs and HRLs enrich the digital news story preservation (DNSP) framework. The Digital News Stories Archive (DNSA) preserves a huge number of news articles from multiple news sources in LRLs and HRLs. This paper presents research challenges encountered during the preservation of Urdu and Arabic-language news articles to create a multilingual news archive. The second part of the paper compares two bilingual linking mechanisms for Urdu-to-English-language news articles in the DNSA: the common ratio measure for dual language (CRMDL) and the similarity measure based on transliteration words (SMTW) with the cosine similarity measure (CSM) baseline technique. The experimental results show that the SMTW is more effective than the CRMDL and CSM for linking Urdu-to-English news articles. The precision improved from 46% and 50% to 60%, and the recall improved from 64% and 67% to 82% for CSM, CRMDL, and SMTW, respectively, with improved impact of common terms as well.

Knowledge-Guided Article Embedding Refinement for Session-Based News Recommendation.

Personalized news recommendation aims to recommend news articles to customers, by exploiting the personal preferences and short-term reading interest of users. A practical challenge in personalized news recommendations is the lack of logged user interactions. Recently, the session-based news recommendation has attracted increasing attention, which tries to recommend the next news article given previous articles in an active session. Current session-based news recommendation methods mainly extract latent embeddings from news articles and user-item interactions. However, many existing methods could not exploit the semantic-level structural information among news articles. And the feature learning process simply relies on the news articles in training data, which may not be sufficient to learn semantically rich embeddings. This brief presents a context-aware graph embedding (CAGE) approach for session-based news recommendation. It employs external knowledge graphs to improve the semantic-level representations of news articles. Moreover, graph neural networks are incorporated to further enhance the article embeddings. In addition, we consider the similarity among sessions and design attention neural networks to model the short-term user preferences. Extensive results on multiple news recommendation benchmark datasets show that CAGE performs better than some competitive baselines in most cases.

Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule

In this study, we investigate the predictive capabilities of different news providers based on sentiment analysis, and propose a framework that endows different weights to different news providers for improving the prediction performance. In sentiment analysis, the prevalent Loughran-McDonald sentiment dictionary is utilized to calculate the sentiment scores of news articles, and the sentiment index of each news provider is obtained by integrating these sentiment scores. Based on the market data and sentiment indices of multiple news providers, we employ the recurrent neural network to build a number of base classifiers, and adopt the evidential reasoning rule to combine these base classifiers for predicting the stock market index movement. Additionally, the genetic algorithm is used to optimize the weights of base classifiers and important hyper-parameters of the recurrent neural network. In the experimental study, we apply the proposed approach to the daily movement prediction of the S&P 500 index, Dow Jones Industrial Average index and NASDAQ 100 index, and compare it with some state-of-the-art methods. The results show that our approach is effective for improving the prediction performance. Besides, the designed trading strategy based on the results of the proposed model achieves higher return rates than other trading strategies.

Die Briefe Philipp Jakob Speners

Abstract Philipp Jakob Spener was one of the most important Lutheran theologians in the last third of the 17th century. For Spener, the letter is a means of information and communication. Spener’s letters contain multiple news items about people, events and new literary publications. He himself published his responsa using excerpts and complete letters for it. Private letters can hardly be found in the printed Responsa collections. However, some of these have been preserved as originals or as copies. The variety of topics is remarkable. Using preserved manuscripts, a historical-critical edition of Spener’s letters is currently being made.

Identifying Early Warning Signals from News Using Network Community Detection

The paper addresses the challenge of accelerating identification of changes in risk drivers in the insurance industry. Specifically, the work presents a method to identify significant news events ("signals") from batches of news data to inform Life & Health insurance decisions. Signals are defined as events that are relevant to a tracked risk driver, widely discussed in multiple news outlets, contain novel information and affect stakeholders. The method converts unstructured data (news articles) into a sequence of keywords by employing a linguistic knowledge graph-based model. Then, for each time window, the method forms a graph with extracted keywords as nodes and draws weighted edges based on keyword co-occurrences in articles. Lastly, events are derived in an unsupervised way as graph communities and scored for the requirements of a signal: relevance, novelty and virality. The methodology is illustrated for a Life & Health topic using news articles from Dow Jones DNA proprietary data set, and assessed against baselines on a publicly available news data set. The method is implemented as an analytics engine in Early Warning System deployed at Swiss Re for the last 1.5 years to extract relevant events from live news data. We present the system's architectural design in production and discuss its use and impact.

Credibility and shareworthiness of negative news

Negativity in the news sells, but is such news also perceived as more credible and shareworthy? Given that negative information is more impactful and processed more easily, a positive-negative asymmetry might also exist in news processing. This negativity bias is explored in a two-part experiment ( N = 696) where respondents rated (a) multiple positive and negative news items and (b) conflicting news on perceived credibility and shareworthiness. Results reveal no straightforward patterns: Audiences only hold a negativity bias in their credibility assessment under certain conditions, and even less so when it comes to sharing news. When confronted with conflicting information, audiences do not seem to use negativity as a cue to determine which news to believe or share.

Effects of Multiple Financial News Shocks on Tourism Demand Volatility Modelling and Forecasting

Even though both symmetric and asymmetric conceptions of news impacts are well-established in the disciplines of economics and financial markets, the effects of combining multiple news shocks on the volatility of tourism demand have not yet been delved into or gauged in any tourist destination. This work hypothesises and verifies that the news impact curve (NIC), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the Malaysian tourist industry. Among them, three primarily volatility models (GARCH, EGARCH, and GJRGARCH) are used in conjunction with five financial news shocks (FFNSs), namely the Kuala Lumpur Composite Index (KLCI), the United States Dollar Index (DXY), the stock performance of 500 large companies listed on stock exchanges (S&P500), Crude Oil (CO), and Gold Price (GP). Among the most significant findings of this study are the demonstration of monthly seasonality using conditional mean equations, asymmetry effects in EGARCH-FFNSs, and GJRGARCH-FFNSs models in conditional variance equations and 50 NICs, and the GARCH-FFNSs model’s evaluation of the persistence influence of news shocks on monthly visitor arrivals in Malaysia. The GJRGARCH-FFNSs model is the best model for Malaysian tourism demand volatility forecasting accuracy. Furthermore, KLCI and Gold Price have the most substantial impact on the number of tourists to Malaysia. In addition, it should be emphasised that the methodological framework utilised in this study can be a useful tool for creating and forecasting the performance of symmetry and asymmetry impacts on tourism demand volatility.

Depiction of mental health issues during Covid-19 pandemic in media: an analysis framework model and a pilot study (the cases of Australia, Germany and Lithuania)

Aim of the studyTo present the new model for the depiction of mental health issues during Covid-19 pandemic in the mediaSubject or material and methodsA unique 11-item framework was created for the analysis of the articles and the pilot study was conducted.Results365 online media articles related to mental health and Covid-19 from multiple news websites in Lithuania, Australia and Germany were included in the pilot study, which showed the power of the proposed model for depicting the mental health issues during Covid-19 pandemicDiscussionThe authors suggest that the proposed model could be used for further studies gathering not only the newspapers but also the other sources of media (podcasts, TV news, radio) to have a wider scope for analysis and interpretation. As a lot of information nowadays is being gathered through social media, this could also be used as a source in future studies, especially to those relating to the younger generation.ConclusionsAs indicated in our pilot study, many articles tend to focus on the adverse effects of the pandemic. Moreover, the news also lacked statistical information, a detailed explanation of the events and solutions to address the issues presented in the articles. With such a high flow of negative information, it is unsurprising that some experts estimate the mental health problems to rise rapidly in the upcoming months and years.

Assessing the Implementation of Open-Door Policy in Managing Syrian Refugees in Germany (2015)

The humanitarian crisis has happened in Syria. Syria has been suffering the dynamics of conflict in their countries spurred by the war under Syrian President Bashar Al-Assad, which caused the forced migration by its citizens to the surrounding countries in 2015 through a massive influx of refugees. Germany is one of Europe's countries experiencing the most significant refugee dynamic”evidenced by the development of refugees in that period. Based on that, this qualitative research aims to understand the fundamental reason for various social events, especially for the German government implementing the Open-Door Policy on Syrian refugees. Methodologically, in data processing technique using library research supported by multiple news portals and scientific journals to help the analysis become very appropriate. As a result, this article signifies Germany's decision regarding the Open-Door Policy on refugees from Syria through human security glasses. It refers to economic security and political security. Densely, Germany provides many opportunities as workers in Germany related to its labor market development. Germany has limited the Syrian refugee's activities and provided by the quality of speaking in Germany like what Angela Merkel has said, instead of talking about the national duty on a humanitarian basis by the policy she did.

Hierarchical Preference Hash Network for News Recommendation

Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.

Memory-Guided Multi-View Multi-Domain Fake News Detection

The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a <i>single</i> domain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news of <i>multiple</i> domains. Based on our analysis, we pose two challenges in multi-domain fake news detection: 1) <bold/><i>domain shift</i><bold/>, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) <bold/><i>domain labeling incompleteness</i><bold/>, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a news piece. In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> FEND) to address these two challenges. We model news pieces from a multi-view perspective, including semantics, emotion, and style. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics. Then, with enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains. Extensive offline experiments on English and Chinese datasets demonstrate the effectiveness of M <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> FEND, and online tests verify its superiority in practice. Our code is available at <uri>https://github.com/ICTMCG/M3FEND</uri>.

News Location Classification

Every day there are a lot of things that happen around the world. There are various ways to record every event that is occurring around the world, such as news, blogs, and articles. Over the past few years, there are multiple news available on every event that has occurred. It adds to the size of information that is available for human beings to consume. People are, moving from paper-based newspapers to digital newspapers to get their daily feed of news and digitization has a role to play in this behaviour. These days every person is preoccupied with a lot of work, online and offline, as mentioned earlier the amount of information is being increased with every passing day. For this reason, people are only interested in news that match their interests. A large amount of data in the form of text is available online, hence its classification based on its hidden features can lead to the better recommendation of news to individuals. In this research work, we have used focus area and temporal features to classify news using a Convolutional Neural Network (CNN). The results of the proposed methodology in the form of precision, accuracy, recall, and F1-Score show that these features indeed can be used for recommender systems.

TBD

The September 2, 2021 edition of The New York Times “The Morning” included an interesting article concerning the collapse of the Champlain Towers South Condo in Miami that resulted in the deaths of 98 people. After seeing multiple news accounts, I couldn’t help but wonder what in the world went wrong? Is there an important system safety lesson to be learned? If so, what might it be? The International System Safety Society (ISSS) is supporting a National Institute for Occupational Safety and Health (NIOSH)-sponsored effort to explore ways to better implement “safety through design” for construction projects. The Times article provided a few enticing tidbits that might be worth mulling over regarding improving safety through design.

Two-channel Attention Mechanism Fusion Model of Stock Price Prediction Based on CNN-LSTM

Using hierarchical CNN, the company's multiple news is characterized as three levels: sentence vectors, chapter vectors, and enterprise sentiment vectors. By combining the stock price data with the news lyric data at the same time, the influence of news on price is used to achieve correlation analysis of news information and stock prices. A two-channel attention mechanism fusion model based on CNN-LSTM is proposed. After the dual-channel feature extraction, the attention layer fusion layer is used to convert the weighted values of LSTM hidden variables, so the stock price can be predicted with the news text.

A Framework for Generating Extractive Summary from Multiple Malayalam Documents

Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand the huge amount of text in a short time; this demands the need for an automatic text summarizer. From summaries, the users get the idea of the entire content of the document and can decide whether to read the entire document or not. This work mainly focuses on generating a summary from multiple news documents. In this case, the summary helps to reduce the redundant news from the different newspapers. A multi-document summary is more challenging than a single-document summary since it has to solve the problem of overlapping information among sentences from different documents. Extractive text summarization yields the sensitive part of the document by neglecting the irrelevant and redundant sentences. In this paper, we propose a framework for extracting a summary from multiple documents in the Malayalam Language. Also, since the multi-document summarization data set is sparse, methods based on deep learning are difficult to apply. The proposed work discusses the performance of existing standard algorithms in multi-document summarization of the Malayalam Language. We propose a sentence extraction algorithm that selects the top ranked sentences with maximum diversity. The system is found to perform well in terms of precision, recall, and F-measure on multiple input documents.

Media Literacy for Elementary Education Students: Inquiry into Fake News

This work focuses on facilitating upper elementary students’ media literacy skills development. Students engage in authentic techniques to recognize and verify media content and sources. Relevant background topics follow this structure: (a) introduction including literature review and purpose, (b) brief history of fake news, (c) impacts of misleading information on society, and (d) how to moderate fake news. The final student inquiry adheres to the College, Career, and Civic Life (C3) Framework for Social Studies. Students investigate these questions: What is fake news? Why should I care? How can fake news affect people and institutions? Why does fake news affect our lives and societies? Interactive, current resources are included to activate students’ abilities to discern multiple fake news concepts and categories. Extra resources accentuate students’ debate skills regarding free speech rights versus media ambiguity. Recommendations for students’ public informed action solutions are discussed.

Decolonising medical education and exploring White fragility.

On 17 August 2020, the BBC journalist Smitha Mundasad1 published a carefully researched article exploring the actions of the University of Bristol Medical School (BRMS) as it seeks to decolonise its curriculum and challenge structural forms of racism. In the subsequent days, this story was adapted and reproduced online by multiple news outlets2,3 who chose to preserve quotes from the only White male member of staff interviewed, exposing an important bias within the media. This article explores the subsequent abuse BRMS members experienced following these publications, why we believe this occurred, and why — despite this — we firmly stand by our commitment to confronting racism in medical education. ### Key concepts The work taking place at BRMS focuses on two key concepts that we believe are integral to achieving a curriculum that is fair to our students and staff, and serves the needs of a modern and diverse NHS. The first of these is ‘decolonisation’, first appearing in academic discourse in 2011 and stemming from the 1990s drive to create more inclusive curricula.4 Championed by students organising the ’ Why is my curriculum so w hite ’ protest,5 it was predominantly driven by the humanities, and has only more recently been applied to the field of medical education. Decolonisation seeks to examine and restructure curriculums that were designed within a colonial mindset, which centralises the White, eurocentric male’s narrative above all others.6 A decolonisation approach to …