News Feeds Research Articles

Advertisement Blindness in Mobile Social Media Apps

Advertisement blindness is a general term that refers to people’s tendency to automatically and unconsciously ignore advertisements. The phenomenon was originally identified in banner ads, then later in text and native ads on websites. Today, social media is an effective tool for advertisers, yet research investigating users’ interaction habits with social media ads in mobile applications is unexplored. This study expands the ad blindness concept to mobile social media apps, examining its presence and whether target position has an influence. To test these questions, we used a search task where participants browsed a mock scrollable Facebook news feed and identified posts that fit certain semantic categories. The study found that participants had higher accuracy in content posts than in ad posts, also ad avoidance was prevalent in the last third of the news feed. Overall, these results revealed the first evidence of ad blindness on social media mobile applications.

Teufelsspuk und Feuerflammen: Pfarrer Josua Finsler (1525-1602) über Unglaubliches aus Biel und Umgebung

The chorherr (canon), Johann Jakob Wick of Zurich drew the material for his voluminous collection of news (Wickiana) from a dense network of intelligence. The information from Bern and Biel was provided here and there by the lesser known citizen of Zurich, deacon Josua Finsler, who lived in Biel. The article spotlights the "Miraculous Sign" of February 1572 in Biel and the diverse interpretations of the event by these two contemporaries. The "Reformed" eschatological understanding of Wick is compared to Finsler’s more rationalist worldview coloured by science.

Genetic monitoring uncovers long-distance marine feeding coupled with strong spatial segregation in sea trout (Salmo trutta L.) consistent at annual and decadal time scales

Abstract Genetic data have greatly increased means to understand fish marine migration behaviours at large spatial scale within a quantitative framework. The anadromous sea trout is a prized target of recreational fishery and an important ecosystem component in freshwater and marine coastal habitats in large parts of temperate northern Europe. Nonetheless, little is known about population distributions while feeding at sea. To reconcile notions about feeding migrations being predominantly locally restricted or not, we used SNP data for 3465 trout representing >100 rivers to identify population origins of 903 coastally feeding fish captured throughout the North Sea-Baltic Sea transition area. Across areas, coastal collections generally showed marked stock-mixing and overall 70:30% native:non-native fish with seasonal variation corresponding with spawning run timing. Data revealed strong spatial feeding segregation between trout from the Scandinavian Peninsula and the European continent. This is surprising given the short distances between areas, but is in alignment with strong genetic differentiation between populations in these areas. Estimation of stock complexity of coastal feeding aggregations showed no spatial trend through the North Sea-Baltic Sea transition area otherwise characterized by strong environmental clines. Analyses of scale samples collected in the 1950s indicate that stock-mixing was consistent over almost 70 years.

User Perceptions of News Recommender Systems and Trust in Media Outlets: A Five-Country Study

ABSTRACT The study investigates user perceptions of news media’s employment of news recommender systems (NRS) and their relation to trust in media outlets. A cross-sectional survey (n = 5079) in the United Kingdom, United States, Poland, the Netherlands, and Switzerland shows that higher algorithmic knowledge, users’ perceived skills, and news aggregator use correspond to increased perceptions that news media use NRS. Moreover, higher perceived NRS use of specific news outlets is associated with lower trust in those outlets but perceived benefits and concerns related to NRS moderate this relationship. Our findings highlight the need for media organizations to ensure a responsible and transparent use of such systems, highlight benefits for users, and address concerns to avoid misperceptions of their NRS use and maintain user trust.

Generation Z media consumption practices in social media

The subject of the work is the media consumption of generation Z in social media. Within the framework of the subject of the study, the author presents a brief theoretical and methodological justification, paying special attention to the aspects of research in the field of media consumption by Russian authors A.V. Sharikov, I.A. Poluekhtova, V.P. Kolomiets, M.M. Nazarova, T.E. Grinberg, and also focuses on how large companies study media consumption of the audience (Analytical Center "Video International", TNS Russia). The focus of the research object is on generation Z. The author examines the boundaries of the generation and outlines the basic values inherent in it, in particular, the value of content consumption in social media, which the author emphasizes, referring to previous research. The study used the in-depth interview method, since it is important to obtain qualitative, rather than quantitative, information to identify the media consumption practices of generation Z in social networks. As a result of the study, the following aspects of media consumption of generation Z were revealed: specific sources of consumption of entertainment, news, educational, humorous content, situations when representatives of generation Z turn to social networks on weekdays and weekends, conditions for abandoning social networks, the problem of choosing content, listing significant bloggers, the need for microinfluencers, openness of new information and boundaries the news feed. The main significant conclusion of the work is that the media consumption of generation Z transforms their usual daily practices: waking up and preparing for bed, cooking and eating, walking and exercise. We can say about the transformation of social networks from platforms for communication into platforms that informatively accompany centenials throughout the day.

Political Communication Strategy in the 2024 Indonesia’s Presidential Election

In every election, candidates employ various techniques to communicate their mission and vision to their respective voters. Winning an election depends on how good the candidate employs the various tools of communication, using both traditional mass media and social media. This research examines the political communication strategy used by Anies Baswedan during the Indonesian Presidential Election 2024. The objective of this research is to study the political communication style used by Anies Baswedan as a non-party political leader in garnering public support and winning the Presidential Elections. All the other presidential candidates contesting in the election are representing various political parties. All the feeds that were published in his Instagram account during the election were analysed. The analysed data is later triangulated with data from key informants to get an insight on Anies Baswedan’s political thoughts. Data from the Instagram account indicates that his team of communicators employs hashtags, news feed, political ads and visual styles to increase awareness among his voters, in the hope of influencing his voters. The various content published is found to be relevant and significant with the voters but is insufficient to win an election. Although he run initially as a non-political party candidate before gaining endorsement from a political party to run for President, he lost could be due to the lack of political infrastructure support that is needed to ensure his words spread throughout vast Indonesia archipelago. Keywords: Strategy, communication, politics, new media, Presidential Election 2024.

Algorithmic Bias and Political Polarization: Analyzing the Role of News Aggregators and Social Media in Pakistan

Algorithmic Bias and Political Polarization: Analyzing the Role of News Aggregators and Social Media in Pakistan

DEVELOPING A COMPREHENSIVE LIVE NEWS AGGREGATOR: INTEGRATING REAL-TIME DATA SOURCES FOR ENHANCED INFORMATION ACCESSIBILITY

Abstract—News Aggregator is simply an online software which collects new stories and events around the world from various sources all in one place. News aggregator plays a very important role in reducing time consumption, as all of the news that would be explored through more than one website will be placed only in a single location. Also, summarizing this aggregated content absolutely will save reader’s time. A proposed technique used called the TextRank algorithm that showed promising results for summarization. This paper presents the main goal of this project which is developing a news aggregator able to aggregate relevant articles of a certain input keyword or key-phrase. Summarizing the relevant articles after enhancing the text to give the reader understandable and efficient summary. Index Term : News aggregator; text summarization; text enhancement; TextRank algorithm

Makemates – Social Media Web Application

"Makemates" is a cutting-edge social media application designed to cater to the diverse needs and desires of modern-day social networking. This all-in-one platform offers a comprehensive suite of basic features that make connecting, sharing, and engaging with others a seamless and enjoyable experience. Users can create and personalize their profiles, connect with friends, family, and likeminded individuals, and effortlessly share their life moments through text, photos, and videos. "Makemates" prioritizes user privacy and data security, providing a safe environment for users to express themselves freely. With features like status updates, messaging, and a news feed, the application enables real-time communication and interaction with an extensive user base. "Makemates" not only fosters connections but also encourages the discovery of new ideas, trends, and experiences. Join the Makemates community and unlock a world of possibilities for social networking, all within one intuitive and user-friendly application.

Summarization and Translation Using NLP

Abstract: Text summarization and translation are two critical tasks in natural language processing with significant applications in various domains such as news aggregation, document summarization, machine translation, and information retrieval. In recent years, there has been remarkable progress in the development of techniques and models for both tasks, leveraging advancements in deep learning and neural network architectures. This paper presents a comprehensive review and comparative analysis of state-of-the-art methods in text summarization and translation. First, we provide an overview of the different approaches to text summarization, including extractive, abstractive, and hybrid methods, highlighting their strengths and weaknesses. We discuss various evaluation metrics and datasets commonly used for benchmarking summarization systems, shedding light on the challenges and opportunities in this field.

Automatic News Summarization using Web Scraping

In an era of information overload, accessing relevant news content efficiently is crucial. However, language barriers often hinder users from accessing news articles in languages they understand. To address this challenge, we propose an automatic news summarization and translation system. This system aggregates news articles from multiple URLs, summarizes them into concise summaries, and translates the summaries into the user's preferred language. By leveraging web scraping, natural language processing, and translation integration techniques, our system aims to provide users with access to relevant news content in their preferred language, thereby overcoming language barriers and fostering global connectivity. Keywords— automatic news summarization, translation integration, web scraping, natural language processing, language barriers, information accessibility, global connectivity, user preferences, multilingual content, web-based news aggregation

News Feed Evolved: A Tale of Ingestion, Curation, and Personalization

News Feed Evolved: A Tale of Ingestion, Curation, and Personalization

Local Variation in Feeding Ground Utilization of Dugongs (Dugong dugon) Across Two Intertidal Seagrass Beds in Talibong Island, Thailand

In this study, we describe the population characteristics and residency patterns of dugongs (Dugong dugon) across two intertidal seagrass beds in Talibong Island, Thailand: Site A, covering an area of 2.0 × 105 m2, and Site B, covering an area of 2.8 × 105 m2. Transect and individual identification surveys were conducted under clear water conditions using drones: 16 separate days over 11 months at Site A and 10 separate days over 3 months at Site B. Sixty-four individuals were identified from 180 videography sessions. The results confirmed at least two distinct patterns of seagrass habitat utilization among sites located approximately 5 km apart. Site A was characterized by a lower population density, higher year-round site fidelity, occupancy by relatively large individuals, and an absence of feeding aggregations. In contrast, Site B was characterized by a higher population density, lower site fidelity, occupancy by individuals with a wider range of body lengths, and the presence of feeding aggregations. The average population density at Site B was three to five times higher than that at Site A. Site A had a median nearest neighbor distance of 320 m with no significant bias in its distribution, whereas Site B had a median of 20 m with a significant bias. The mean site fidelity index for Site A (0.62 ± 0.08; n = 16) was significantly higher than that for Site B (0.39 ± 0.14; n = 10). Dugongs at Site A might have monopolized this site to some extent, while those at Site B might have benefited from increased opportunities for social interaction provided by aggregations. These findings highlight the importance of fine-scale monitoring of feeding ground utilization by dugongs, taking into consideration individual-specific details such as body lengths and resighting rates for a better understanding of their spatial distribution.

Real-Time News Aggregation Using ReactJS: A Case Study and Analysis

Abstract: Real-time news aggregation has become increasingly vital in the digital age, where access to upto-date information is crucial for individuals and organizations alike. This paper presents a comprehensive study on the development of a real-time news application using ReactJS, a popular JavaScript library for building user interfaces. The study explores the significance of real-time updates in news applications, reviews existing literature on news aggregation platforms and ReactJS development, and identifies gaps in the current research landscape. The proposed system leverages ReactJS's flexibility and efficiency to create an intuitive user interface for accessing and interacting with real-time news content. Through integration with external APIs such as News API, the application fetches news articles from various sources and presents them to users in a timely manner.

A review of challenges, algorithms and evaluation methods in news recommendation

News reading is an important social activity and to help readers quickly find news articles of their interest, news content providers and aggregators use recommender systems. Such systems are designed to address a variety of challenges. Inspiration for algorithmic design is taken from various domains which has resulted in the creation of an enormous body of literature. Also, different methods are used for evaluation of the recommendation algorithms. In this study, we review these developments and present three major components in news recommendation research. First, we list and categorise the challenges faced while designing news recommender systems. We especially list the different algorithmic designs used for generating personalised and non-personalised recommendations. We discuss the major neural network architectures that are being increasingly used for both collaborative and content-based recommender systems. Next, we list the two major evaluation methods and also list some popular datasets used in evaluation. Finally, we identify the emerging trends in news recommender research. We find that the issues related to fake news, trust and use of personal data for news recommendation are gaining wider attention, and deep learning methods are being increasingly used to address these issues.

A framework for the analysis of historical newsreels

Audiovisual news is a critical cultural phenomenon that has been influencing audience worldviews for more than a hundred years. To understand historical trends in multimodal audiovisual news, we need to explore them longitudinally using large sets of data. Despite promising developments in film history, computational video analysis, and other relevant fields, current research streams have limitations related to the scope of data used, the systematism of analysis, and the modalities and elements to be studied in audiovisual material and its metadata. Simultaneously, each disciplinary approach contributes significant input to research reducing these limitations. We therefore advocate for combining the strengths of several disciplines. Here we propose a multidisciplinary framework for systematically studying large collections of historical audiovisual news to gain a coherent picture of their temporal dynamics, cultural diversity, and potential societal effects across several quantitative and qualitative dimensions of analysis. By using newsreels as an example of such complex historically formed data, we combine the context crucial to qualitative approaches with the systematicity and ability to cover large amounts of data from quantitative methods. The framework template for historical newsreels is exemplified by a case study of the “News of the Day” newsreel series produced in the Soviet Union during 1944–1992. The proposed framework enables a more nuanced analysis of longitudinal collections of audiovisual news, expanding our understanding of the dynamics of global knowledge cultures.

Are you always ignoring attitude-challenging messages? Eye-tracking selective exposure on Chinese’ lowly involved news consumption

This study employs a pre-post-test design to investigate the selective exposure behaviors while consuming news with low involvement. The experiment incorporates eye-tracking and self-report measures to explore participants’ preferences for attitude-consistent and attitude-inconsistent information. The results of the study indicate that attitude consistency does not serve as the primary determinant of selective exposure; instead, participants demonstrate a stronger inclination towards engaging with a smaller number of posts from a particular perspective. Following their exposure to the news feed, participants display a heightened tendency to read articles with neutral attitudes. In addition, individuals who initially held a particular attitude tend to shift towards a neutral standpoint, particularly when exposed to a mostly-consistent opinion climate. Notably, participants’ attitudes do not tend to become radicalized under any opinion climate. The study acknowledges its limitations and proceeds to discuss the implications of these findings.

A Quantum Approach to News Verification from the Perspective of a News Aggregator

In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new—to the best of our knowledge—algorithm that uses quantum “entanglement” (think of it as a special connection) to help news aggregators “sniff out” bad actors, whether they are other news sources or even fact-checkers trying to spread misinformation. This algorithm does not rely on quantum signatures; it merely uses basic quantum technology which we already have, in particular, special pairs of particles called “EPR pairs” that are much easier to create than other options. More elaborate entangled states are like juggling too many balls—they are difficult to make and slow things down, especially when many players are involved. So, we adhere to Bell states, the simplest form of entanglement, which are easy to generate no matter how many players are involved. This means that our algorithm is faster to set up, works for any number of participants, and is more practical for real-world use. Additionally, as a “bonus point”, it finishes in a fixed number of steps, regardless of how many players are involved, making it even more scalable. This new approach may lead to a powerful and efficient way to fight misinformation in the digital age, using the weird and wonderful world of quantum mechanics.

Competing for attention on digital platforms: The case of news outlets

AbstractResearch SummaryPlatforms are often assumed to benefit firms, especially smaller ones, by facilitating access to a broader consumer base and increasing visibility. However, this logic relies on platforms” ability to match consumer preferences to complement characteristics. In addition to this matching mechanism, we posit that platforms also broker consumer attention towards complements, which then compete for this attention. We propose that this attention mechanism is particularly prominent in settings where complement characteristics cannot be observed ex‐ante, and argue that complementors' with larger scale and broader scope are better positioned to capture attention than smaller and less broad ones. We formalize and test this intuition in the context of news aggregators, highlighting the significance of complementors' ability to draw attention in evaluating their benefits from platform participation.Managerial SummarySmall firms are often assumed to benefit most from joining a platform to expand their market reach and visibility. However, this will only be the case if the main function of platforms is to match consumer preferences to product characteristics. We argue that platforms also direct attention towards some products at the expense of others on the platform. This “attention mechanism” is particularly important whenever product characteristics cannot be observed prior to consumption, and we propose that in such settings, larger scale and broader scope of products drive attention towards specific firms on the platform. We test these predictions in the context of online news aggregators, which feature news content by newspapers of different sizes and with different range of articles. We find that indeed large and generalist newspapers benefit most from being on a news aggregators, while small and focused newspapers perform better when they are not featured on the news aggregator at all.

Model development of the system for avoiding echo chambers in social networks

This article examines the problems that appear in the process of the development of social networks. The focus of the study is on the echo chamber effect in various social networks. It turned out that the echo chamber effect is the result of the work of a deep neural network, which analyzes the interests and priorities of each social network user, checks other similar posts and priorities of other users, and then forms a “circle” of like-minded people for each user. Thus, the main drawback of the standard content generation algorithm is the selection of only publications and comments that support the user’s position. The goal of the study is a software architecture to solve the echo chamber problem in highly loaded social networks in real-time. The main idea was to cluster opinions on controversial topics and include user opinions from different clusters in user content. Data on controversial topics is collected from news, scholarly articles, publications, and comments with hashtags. The collected topics are clustered usi ng the K-means algorithm and the “elbow” method is used to find the optimal number of clusters. The result of the clustering of opinions by topic is provided as input data for the generation of a news feed by a recurrent neural network. The system uses Kafka as a message broker between microservices and AZURE blob storage for storing publications and comments. Both solutions support high scalability.