scholarly journals Proppy: A System to Unmask Propaganda in Online News

2019 ◽  
Vol 33 ◽  
pp. 9847-9848 ◽  
Author(s):  
Alberto Barrón-Cedeño ◽  
Giovanni Da San Martino ◽  
Israa Jaradat ◽  
Preslav Nakov
Keyword(s):  
Real Time ◽  
Real World ◽  
Detection System ◽  
Online News ◽  
News Sources ◽  
Raising Awareness ◽  
The Impact

We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation. The system constantly monitors a number of news sources, deduplicates and clusters the news into events, and organizes the articles about an event on the basis of the likelihood that they contain propagandistic content. The system is trained on known propaganda sources using a variety of stylistic features. The evaluation results on a standard dataset show stateof-the-art results for propaganda detection.

scholarly journals Comparison of Traditional Citation Metrics and Altmetrics Among Dermatology Journals: Content and Correlational Analysis Study (Preprint)

2019 ◽  
Author(s):  
Gregg Murray ◽  
Rebecca Hellen ◽  
James Ralph ◽  
Siona Ni Raghallaigh
Keyword(s):  
Social Media ◽  
Citation Count ◽  
Research Impact ◽  
Online News ◽  
Positive Correlation ◽  
News Sources ◽  
Journal Citation Reports ◽  
Social Media Platforms ◽  
The Impact ◽  
Article Citation

BACKGROUND Research impact has traditionally been measured using citation count and impact factor (IF). Academics have long relied heavily on this form of metric system to measure a publication’s impact. A higher number of citations is viewed as an indicator of the importance of the research and a marker for the impact of the publishing journal. Recently, social media and online news sources have become important avenues for dissemination of research, resulting in the emergence of an alternative metric system known as altmetrics. OBJECTIVE We assessed the correlation between altmetric attention score (AAS) and traditional scientific impact markers, namely journal IF and article citation count, for all the dermatology journal and published articles of 2017. METHODS We identified dermatology journals and their associated IFs available in 2017 using InCites Journal Citation Reports. We entered all 64 official dermatology journals into Altmetric Explorer, a Web-based platform that enables users to browse and report on all attention data for every piece of scholarly content for which Altmetric Explorer has found attention. RESULTS For the 64 dermatology journals, there was a moderate positive correlation between journal IF and journal AAS (<i>r<sub>s</sub></i>=.513, <i>P</i>&lt;.001). In 2017, 6323 articles were published in the 64 dermatology journals. Our data show that there was a weak positive correlation between the traditional article citation count and AAS (<i>r<sub>s</sub></i>=.257, <i>P</i>&lt;.001). CONCLUSIONS Our data show a weak correlation between article citation count and AAS. Temporal factors may explain this weak association. Newer articles may receive increased online attention after publication, while it may take longer for scientific citation counts to accumulate. Stories that are at times deemed newsworthy and then disseminated across the media and social media platforms border on sensationalism and may not be truly academic in nature. The opposite can also be true.

SOCIAL NETWORK NEWS SENTIMENTS AND STOCK PRICE MOVEMENT: A CORRELATION ANALYSIS

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Anupong Sukprasert ◽  
Kasturi Kanchymalay ◽  
Naomie Salim ◽  
Atif Khan
Keyword(s):  
Real Time ◽  
Stock Prices ◽  
Stock Price ◽  
R Package ◽  
Online News ◽  
Streaming Data ◽  
Price Movement ◽  
News Sources ◽  
R Programming ◽  
The Relationship

The stock market prediction is one of the most important issues extensively investigated in the existing academic literatures. Researchers have discovered that real–time news has much bearing on the movement of stock prices. Analysts now have to deal with vast amounts of real time, unstructured streaming data due to the advent of electronic and online news sources. This paper aims to investigate the relationship between online news and actual stock price movement.  R programming together with R package are applied to capture and analyze the online news data from Yahoo Financial. The data are plotted into graphs to analyze the relationship between the two variables. In addition, to ensure the levels of the relationship, the Pearson’s correlation and Spearman’s Rank are applied to test whether there is a statistical association between these two variables. This initial analysis of dynamic online news based on sentimental words is relatively constructive.

A new transnational arena? An analysis of cross-border web traffic towards professional online news sites in the Arab world

2019 ◽  
Vol 12 (2) ◽  
pp. 145-167 ◽  
Author(s):  
Andrea Haeuptli
Keyword(s):  
United Arab Emirates ◽  
Arab World ◽  
Online News ◽  
Arab Countries ◽  
Traffic Flows ◽  
Web Traffic ◽  
Cross Border ◽  
News Sources ◽  
Leading Role ◽  
The Impact

In recent years, Arab news industries have been confronted with an unparalleled increase in demand for journalistic offers. In parallel, Internet penetration throughout the Arab world has increased significantly, leading to a shift of consumption away from traditional channels towards the digital realm. This article addresses the impact of those recent developments on a shared transnational communicative arena throughout the Arab world. It includes geographically disaggregated traffic data of 630 inductively collected professional online news sources. Using a network analysis approach, it has been assessed that indeed, cross-border consumption of professional online news is a common and general feature in the region. Traffic flows between the countries are highly diversified without patterns of sub-segmentation. At the same time, the strength of traffic flows reflects the traditional leading role of the media industries in the United Arab Emirates, Egypt, Saudi Arabia and Qatar. Yet, weaker traffic flows between the other Arab countries are common and diverse, leading to a high overall integration of the Arab transnational communicative arena within the digital realm.

Day-to-Day Learning Framework for Online Origin–Destination Demand Estimation and Network State Prediction

2019 ◽  
Vol 2673 (11) ◽  
pp. 195-208
Author(s):  
Eunhye Kim ◽  
Hani S. Mahmassani ◽  
Haleh Ale-Ahmad ◽  
Marija Ostojic
Keyword(s):  
Real Time ◽  
Real World ◽  
A Priori ◽  
Demand Estimation ◽  
Prediction Performance ◽  
Traffic Prediction ◽  
Base Case ◽  
Consistency Checking ◽  
Learning Framework ◽  
The Impact

Origin–destination (O–D) demand is a critical component in both online and offline dynamic traffic assignment (DTA) systems. Recent advances in real-time DTA applications in large networks call for robust and efficient methodologies for online O–D demand estimation and prediction. This study presents a day-to-day learning framework for a priori O–D demand, along with a predictive data-driven O–D correction approach for online consistency between predicted and observed (sensor) values. When deviations between simulation and real world are observed, a consistency-checking module initiates O–D demand correction for the given prediction horizon. Two predictive correction methods are suggested: 1) simple gradient method, and 2) Taylor approximation method. New O–D demand matrices, corrected for 24 simulation hours by the correction module, are used as the updated a priori demand for the next day simulation. The methodology is tested in a real-world network, Kansas City, MO, for a 3-day period. Actual tests in real-world networks of online DTA systems have been very limited in the literature and in actual practice. The test results are analyzed in time and space dimensions. The overall performance of observed links is assessed. To measure the impact of O–D correction and daily O–D updates, traffic prediction performance with the new modules is compared with the base case. Predictive O–D correction improves prediction performance in a long prediction window. Also, daily updated O–D demand provides better initial states for traffic prediction, enhancing prediction in short prediction windows. The two modules collectively improve traffic prediction performance of the real-time DTA system.

Using Mobile, Wearable, Technology to Understand the Role of Built Environment Demand for Outdoor Mobility

2018 ◽  
Vol 51 (6) ◽  
pp. 671-688 ◽  
Author(s):  
Kate Duchowny ◽  
Philippa Clarke ◽  
Nancy Ambrose Gallagher ◽  
Robert Adams ◽  
Andrea L. Rosso ◽  
...  
Keyword(s):  
Built Environment ◽  
Real Time ◽  
Real World ◽  
Assessment Tool ◽  
Community Dwelling ◽  
Motion Sensors ◽  
Convenience Sample ◽  
Mobility Data ◽  
Environmental Demand ◽  
The Impact

Walking outdoors requires navigating a complex environment. However, no studies have evaluated how environmental barriers affect outdoor mobility in real time. We assessed the impact of the built environment on outdoor mobility, using mobile, wearable inertial measurement units. Data come from a convenience sample of 23 community-dwelling adults in Southeast Michigan. Participants walked a defined outdoor route where gait metrics were captured over a real-world urban environment with varying challenges. Street segments were classified as high versus low environmental demand using the Senior Walking Environmental Assessment Tool. Participants ranged in age from 22 to 74 years (mean age of 47 years). Outdoor gait speed was 0.3 m/s slower, and gait variability almost doubled, over the high- versus low-demand environments (coefficient of variability = 10.6% vs. 5.6%, respectively). This is the first study to demonstrate the feasibility of using wearable motion sensors to gather real-time mobility data in response to outdoor environmental demand. Findings contribute to the understanding of outdoor mobility by quantifying how real-world environmental challenges influence mobility in real time.

scholarly journals Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management

2021 ◽  
Vol 18 (22) ◽  
pp. 12172
Author(s):  
Abdelghani Ghanem ◽  
Chaimae Asaad ◽  
Hakim Hafidi ◽  
Youness Moukafih ◽  
Bassma Guermah ◽  
...  
Keyword(s):  
Social Media ◽  
Real Time ◽  
Topic Modeling ◽  
Language Model ◽  
Online News ◽  
Machine Learning Classification ◽  
Human Interactions ◽  
Universal Language ◽  
Health Related ◽  
The Impact

The impact of COVID-19 on socio-economic fronts, public health related aspects and human interactions is undeniable. Amidst the social distancing protocols and the stay-at-home regulations imposed in several countries, citizens took to social media to cope with the emotional turmoil of the pandemic and respond to government issued regulations. In order to uncover the collective emotional response of Moroccan citizens to this pandemic and its effects, we use topic modeling to identify the most dominant COVID-19 related topics of interest amongst Moroccan social media users and sentiment/emotion analysis to gain insights into their reactions to various impactful events. The collected data consists of COVID-19 related comments posted on Twitter, Facebook and Youtube and on the websites of two popular online news outlets in Morocco (Hespress and Hibapress) throughout the year 2020. The comments are expressed in Moroccan Dialect (MD) or Modern Standard Arabic (MSA). To perform topic modeling and sentiment classification, we built a first Universal Language Model for the Moroccan Dialect (MD-ULM) using available corpora, which we have fine-tuned using our COVID-19 dataset. We show that our method significantly outperforms classical machine learning classification methods in Topic Modeling, Emotion Recognition and Polar Sentiment Analysis. To provide real-time infoveillance of these sentiments, we developed an online platform to automate the execution of the different processes, and in particular regular data collection. This platform is meant to be a decision-making assistance tool for COVID-19 mitigation and management in Morocco.

scholarly journals Comparison of Traditional Citation Metrics and Altmetrics Among Dermatology Journals: Content and Correlational Analysis Study

10.2196/15643 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e15643
Author(s):  
Gregg Murray ◽  
Rebecca Hellen ◽  
James Ralph ◽  
Siona Ni Raghallaigh
Keyword(s):  
Social Media ◽  
Citation Count ◽  
Research Impact ◽  
Online News ◽  
Positive Correlation ◽  
News Sources ◽  
Journal Citation Reports ◽  
Social Media Platforms ◽  
The Impact ◽  
Article Citation

Background Research impact has traditionally been measured using citation count and impact factor (IF). Academics have long relied heavily on this form of metric system to measure a publication’s impact. A higher number of citations is viewed as an indicator of the importance of the research and a marker for the impact of the publishing journal. Recently, social media and online news sources have become important avenues for dissemination of research, resulting in the emergence of an alternative metric system known as altmetrics. Objective We assessed the correlation between altmetric attention score (AAS) and traditional scientific impact markers, namely journal IF and article citation count, for all the dermatology journal and published articles of 2017. Methods We identified dermatology journals and their associated IFs available in 2017 using InCites Journal Citation Reports. We entered all 64 official dermatology journals into Altmetric Explorer, a Web-based platform that enables users to browse and report on all attention data for every piece of scholarly content for which Altmetric Explorer has found attention. Results For the 64 dermatology journals, there was a moderate positive correlation between journal IF and journal AAS (rs=.513, P<.001). In 2017, 6323 articles were published in the 64 dermatology journals. Our data show that there was a weak positive correlation between the traditional article citation count and AAS (rs=.257, P<.001). Conclusions Our data show a weak correlation between article citation count and AAS. Temporal factors may explain this weak association. Newer articles may receive increased online attention after publication, while it may take longer for scientific citation counts to accumulate. Stories that are at times deemed newsworthy and then disseminated across the media and social media platforms border on sensationalism and may not be truly academic in nature. The opposite can also be true.

scholarly journals Software-in-Loop Simulation Environment for Electromagnetic Underwater Wireless Sensor Networks over STANAG 5066 Protocol

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1611
Author(s):  
Victor Alonso-Eugenio ◽  
Victor Guerra ◽  
Santiago Zazo ◽  
Ivan Perez-Alvarez
Keyword(s):  
Real Time ◽  
Real World ◽  
Network Performance ◽  
Wireless Sensor ◽  
Critical Parameters ◽  
Protocol Stack ◽  
Simulation Environment ◽  
Underwater Wireless Sensor Networks ◽  
The Impact ◽  
Wireless Node

In this work, the development of a software-in-loop platform to carry out Underwater Wireless Sensor Network (UWSN) simulations using a real-time STANAG 5066 stack is presented. The used protocol stack is part of a real-world implementation of an underwater wireless node based on ElectroMagnetic (EM) Underwater Radio Frequency Communication (EM-URFC), framed within Spanish Government’s project HERAKLES. The main objective of this work was to assess the suitability of this software-in-loop approach for carrying out realistic UWSN simulations. In addition to a detailed description of the simulation process, several simulations considering an illustrative network topology are performed, analyzing the impact of different critical parameters on the network performance. The conclusions suggest that the developed software-in-loop platform is suitable to carry out UWSN network tests using a real-world implementation of the STANAG 5066 stack. Moreover, other real-time protocol stacks may be easily adapted with minor modifications.

scholarly journals Media Roles in the Online News Domain: Authorities and Emergent Audience Brokers

2020 ◽  
Vol 8 (2) ◽  
pp. 98-111 ◽  
Author(s):  
Sílvia Majó-Vázquez ◽  
Ana S. Cardenal ◽  
Oleguer Segarra ◽  
Pol Colomer De Simón
Keyword(s):  
News Media ◽  
Search Algorithm ◽  
Dominant Role ◽  
Online News ◽  
Media Ecology ◽  
Research Attention ◽  
Navigation Data ◽  
News Sources ◽  
The Impact

This article empirically tests the role of legacy and digital-born news media, mapping the patterns of audience navigation across news sources and the relationship between news providers. We borrow tools from network science to bring evidence that suggest legacy news media retain control of the most central positions in the online news domain. Great progress has been made in discussing theoretically the impact of the Internet on the news media ecology. Less research attention, however, has been given to empirically testing changes in the role of legacy media and the rising prominence of digital-born outlets. To fill this gap, in this study we use the hyperlink-induced topic search algorithm, which identifies authorities by means of a hyperlink network, to show that legacy media are still the most authoritative sources in the media ecology. To further substantiate their dominant role, we also examine the structural position of news providers in the audience network. We gather navigation data from a panel of 30,000 people and use it to reproduce the network of patterns of news consumption. While legacy news media retain control of the brokerage positions for the general population, our analysis—focused on patterns of young news consumers—reveals that new digital outlets also occupy relevant positions to control the audience flow. The results of this study have substantive implications for our understanding of news organizations’ roles and how they attain authority in the digital age.

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