scholarly journals Gender (in)equality in Chilean press: journalists and sources

2021 ◽  
Vol 20 (2) ◽  
pp. 259-275
Author(s):  
Marta Mensa Torras ◽  
Matthieu Vernier ◽  
Luís Cárcamo-Ulloa ◽  
Fabían Ruíz ◽  
Boris Sotomayor-Gómez
Keyword(s):  
Gender Bias ◽  
Language Processing ◽  
Computational Social Science ◽  
Significant Finding ◽  
Social Phenomena ◽  
Female Journalists ◽  
The Media ◽  
Processing Techniques ◽  
Media Power ◽  
Social Science Approach

Who writes the news in the Chilean press according to gender? Who are the sources, male or female, in the Chilean press? Is there a relationship between the gender of journalists and the gender of the sources in the Chilean press? This article studies the gender of the Chilean newsroom and their sources in 12,113 news through a quantitative method with a computational social science approach. This method combines web scraping and natural language processing techniques to gather and preprocess data, facilitating the exploration of complex social phenomena. Results show important biases in journalists and source gender. From a sample of 158 journalists, 99 were men (63%) and 59 women (37%). Also, from 12,113 news, 7,565 (62%) were written by male and 4,548 (38%) by female journalists. Of the 12,334 sources mentioned in the news, 9,771 were men (79%) and 2,563 were women (21%). A significant finding is that equality in the newsroom is related to how female and male journalists choose their sources. In other words, when a media has a newsroom with gender equality, the sources of the journalists are more equitable too. These results have important insights to discuss within the journalism schools, to make students aware of the gender bias in the profession. Furthermore, if the presence of female – journalists or sources- increased in the media, it would allow them to grow their media power and status.

Cobertura de diarios en Twitter de las elecciones autonómicas de Madrid 2021. Procesamiento de lenguaje natural y algoritmos de aprendizaje automático

2021 ◽  
Author(s):  
Alba Córdoba-Cabús ◽  
Manuel Hidalgo-Arjona ◽  
Álvaro López-Martín
Keyword(s):  
Language Processing ◽  
Media Coverage ◽  
General Pattern ◽  
News Coverage ◽  
Machine Learning Algorithms ◽  
The Media ◽  
Negative Messages ◽  
Processing Techniques ◽  
Emotional Value ◽  
Political Alignment

The aim of this work is to study the news coverage by the main Spanish generalist newspapers on Twitter during the campaign for the Community of Madrid elections in 2021 (n = 2,709). Natural language processing techniques and machine learning algorithms are applied to identify the predominant topic related to the elections and the mentions of candidates and political parties by each media, and to calibrate the emotional value of the messages published by El país, El mundo, Eldiario.es, and El confidencial. Among other findings, the results reveal how the media coverage focused mainly on campaign events and electoral debates. Despite the detection of minor differences between the newspapers, a general pattern emerges through this content, with a notable dominance of Isabel Díaz Ayuso but little influence of Rocío Monasterio as the lead candidates for their party. The sentiment analysis reveals the political alignment of each newspaper, using mainly negative messages with the aim of reducing the importance of a candidate or political party. While El país and Eldiario.es focused their criticism on Vox and the Partido Popular, El mundo and El confidencial criticized the actions of the national government, the PSOE’s proposal to join forces with Unidas Podemos, and Vox’s position, as well as emphasizing the disaster faced by Ciudadanos. It can be deduced that the media contributed to Ayuso’s success and to the consolidation of her image as an individual distinct from her own party. Resumen Se examina la cobertura informativa en las principales cabeceras generalistas españolas en Twitter durante la campaña de las elecciones a la Comunidad de Madrid en 2021 (n=2.709). Mediante técnicas de procesamiento de lenguaje natural y algoritmos de aprendizaje automático se identifica el tema preponderante vinculado a los comicios, se señala la incidencia de los candidatos y los partidos en cada medio y se calibra el valor emocional de los mensajes publicados por El país, El mundo, Eldiario.es y El confidencial. Entre otras pesquisas, los resultados evidencian cómo la cobertura mediática se centra, principalmente, en los actos de campaña y en los debates electorales. Pese a detectar pequeñas disimilitudes entre los diarios, se intuye un patrón generalizado: la notable incidencia de Isabel Díaz Ayuso y la escasa influencia de Rocío Monasterio como cabeza de lista en el contenido. A partir del análisis de sentimientos se constata la alineación partidista de las cabeceras, exponiendo, sobre todo, mensajes en tono negativo con la intención de mermar la relevancia de algún candidato o formación política. Mientras El país y Eldiario.es situaron en el centro de sus críticas a Vox y al Partido Popular, El mundo y El confidencial recriminaron la actuación del gobierno central, menospreciaron la proposición del PSOE a Unidas Podemos en aras de aunar votos, censuraron la postura de Vox e, incluso, insistieron en la debacle de Ciudadanos. Se deduce que los propios medios han contribuido al modelo de éxito de Ayuso y a la consolidación de su figura, propiciando ese individualismo y el distanciamiento de las siglas de su propio partido.

scholarly journals Following the footsteps of giants: modeling the mobility of historically notable individuals using Wikipedia

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Lorenzo Lucchini ◽  
Sara Tonelli ◽  
Bruno Lepri
Keyword(s):  
Language Processing ◽  
Human Mobility ◽  
Computational Social Science ◽  
Cultural Development ◽  
Mobility Patterns ◽  
Historical Information ◽  
Steady Growth ◽  
International Collaborations ◽  
Processing Techniques

AbstractThe steady growth of digitized historical information is continuously stimulating new different approaches to the fields of Digital Humanities and Computational Social Science. In this work we use Natural Language Processing techniques to retrieve large amounts of historical information from Wikipedia. In particular, the pages of a set of historically notable individuals are processed to catch the locations and the date of people’s movements. This information is then structured in a geographical network of mobility patterns.We analyze the mobility of historically notable individuals from different perspectives to better understand the role of migrations and international collaborations in the context of innovation and cultural development. In this work, we first present some general characteristics of the dataset from a social and geographical perspective. Then, we build a spatial network of cities, and we model and quantify the tendency to explore of a set of people that can be considered as historically and culturally notable. In this framework, we show that by using a multilevel radiation model for human mobility, we are able to catch important features of migration’s behavior. Results show that the choice of the target migration place for historically and culturally relevant people is limited to a small number of locations and that it depends on the discipline a notable is interested in and on the number of opportunities she/he can find there.

When Digital Trace Data Meet Traditional Communication Theory: Theoretical/Methodological Directions

2018 ◽  
Vol 38 (1) ◽  
pp. 91-107 ◽  
Author(s):  
Sujin Choi
Keyword(s):  
Language Processing ◽  
Communication Theory ◽  
Longitudinal Change ◽  
Communication Research ◽  
Human Communication ◽  
Power Law Distribution ◽  
Trace Data ◽  
Automated Content Analysis ◽  
Processing Techniques ◽  
Social Science Approach

This study suggests one direction of theoretical and methodological coupling of communication research with the digital trace data, utilizing its differences from the traditional social science approach (e.g., sampling vs. population, normal distribution vs. power–law distribution, generalization vs. simulation, deductive vs. inductive, and perceived vs. actual). We propose specific examples of (i) combining communication research with trace data methodologically and theoretically; (ii) collaborating with linguistic psychology complemented with the automated content analysis and natural language processing techniques; and (iii) creating new theoretical inquiries by configuring the granular level of interactivity and underlying dynamics, observing the longitudinal change of interactions, and discovering the neglected presence of outliers and the invisibles. We expect the direction suggested by this study contributes to deepening our understanding of human communication behavior.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

Exploring Library Core Competencies: A Text Mining Study of American Library Association’s Job Advertisements From 2006 Through 2017

2021 ◽  
pp. 089443932110272
Author(s):  
Qinghong Yang ◽  
Zehong Shi ◽  
Yan Quan Liu
Keyword(s):  
Language Processing ◽  
Work Experience ◽  
Core Competencies ◽  
Core Competency ◽  
Analytical Investigation ◽  
Market Demand ◽  
American Library ◽  
Job Titles ◽  
Job Advertisements ◽  
Processing Techniques

Are core competency requirements for relevant positions in the library shifting? Applying natural language processing techniques to understand the current market demand for core competencies, this study explores job advertisements issued by the American Library Association (ALA) from 2006 to 2017. Research reveals that the job demand continues to rise at a rate of 13% (2006–2017) and that the requirements for work experience are substantially extended, diversity of job titles becomes prevalent, and rich service experience and continuous lifelong learning skills are becoming more and more predominant for librarians. This analytical investigation informs the emerging demands in the American job market debriefing the prioritization and reprioritization of the current core competency requirements for ALA librarians.

scholarly journals Methods, Developments, and Technological Innovations for Population Surveys

2021 ◽  
pp. 089443932199421
Author(s):  
Venera Tomaselli ◽  
Sebastiano Battiato ◽  
Alessandro Ortis ◽  
Giulio G. Cantone ◽  
Salvatore Urso ◽  
...  
Keyword(s):  
Population Studies ◽  
Data Gathering ◽  
Computational Social Science ◽  
Technological Innovations ◽  
Computer Assisted ◽  
Respondent Driven Sampling ◽  
Population Surveys ◽  
Panel Management ◽  
Dominant Paradigm ◽  
Social Science Approach

This article reviews contemporary issues in survey research, connecting established methods to innovative tools and technologies like real-time sensors and computer vision. This link takes into account the idea about the “organical” nature of Big Data, which represents a challenge toward a modernization of population studies in the light of technological innovations. The adopted dominant paradigm of data gathering is web survey (computer-assisted web interviewing), which is explored through the formalization of chain-referral methods as respondent-driven sampling. The general orientation is toward a computational social science approach. Weaknesses of such methodology is studied and solutions are provided with insights from empirical research on panel management. Contribution from gamification techniques is critically discussed.

“Germany in ruins”. Framing new political movements in Germany in the Polish opinion-forming press

2019 ◽  
Vol 15 (1) ◽  
pp. 97-119
Author(s):  
Karol Franczak
Keyword(s):  
Social Life ◽  
Public Affairs ◽  
New Right ◽  
Political Movements ◽  
Framing Analysis ◽  
Critical Perspective ◽  
Social Phenomena ◽  
The Public ◽  
The Media ◽  
Contemporary Media

Abstract One of the main goals of contemporary media, along with the experts and professionals, who speak in them, has been to explain complex issues and provide the audience with clear descriptions of social reality. This is mostly achieved by the production of ideologically useful interpretative schemes that facilitate understanding of the issues present on the media agenda. An important strategy of shaping the public opinion in the way in which public affairs and the activity of social life participants is framed. Analyses of such practices have been conducted for over thirty years within various research approaches collectively referred to as framing analysis. This research provides several arguments helping one to develop a more critical perspective on the representations of social phenomena dominant in the media and discourses of symbolic elites (e.g. opinion writers, academics, experts, journalists, politicians), along with the analyses of the origin of such phenomena, moral judgements and preferred "corrective policies". One of the phenomena defined by the media in Europe as the most important one for the past several years, is the so-called "New Right". The aim of the paper is to analyse the interpretative schemes used by the journalists of four Polish opinion-forming weeklies and to describe the activity of its German manifestation – the Patriotic Europeans Against the Islamisation of the Occident (Pegida) social movement and the Alternative for Germany party (AfD).

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