News Volume Research Articles

Linguistic differences in the political communication of winning mayors in Colombia’s local elections between 2019 and 2023 / Diferencias lingüísticas en la comunicación política de gobernantes ganadores en las elecciones locales 2019–23 en Colombia

The use of language reflects different sociocognitive processes which are crucial in political psychology. In political communication in elections and governance, the manipulation of language to mobilize actions may be imperceptible by citizens, especially in the vast volumes of news posted on digital platforms like Twitter (now X). For this reason, we set out to analyse the differences in the linguistic style and content of the messages of 18 mayors of large cities in Colombia during the electoral campaign and the start of the period of governance through a two-stage design. We found that the politicians’ language during the electoral campaign had a persuasive function focused on promoting inclusive identities and generating empathy using prosocial topics. In contrast, the linguistic style during the term of office was more formal and complex, highlighting the legitimacy of the acts of governance. Differences were also found in the candidates on the left and right related to how they discuss economic issues during the electoral campaign. These findings show the need to take a multidisciplinary approach to address the effects of language and technology on the understanding and explanation of the psycho-political processes involved in elections and governance.

Fooling Them, Not Me? How Fake News Affects Evaluators’ Reputation Judgments and Behavioral Intentions

The volume of fake news in the digital media landscape is increasing, creating a new threat to organizations’ reputations. At the same time, individuals are more aware of the existence of fake news. It thus remains unclear how fake news affects evaluators’ reputation judgments. In this article, we draw on the distinction between first-order judgments (i.e., an individual evaluator’s reputation judgment) and second-order judgments (i.e., an individual evaluator’s belief about the reputation judgments of other evaluators). We integrate this distinction with insights from communication research and social psychology to theorize how fake news affects reputation judgments and behavioral intentions. Through three experimental studies, we show that the negative effect of fake news is larger for second-order reputation judgments and that this effect is greater for organizations with a positive reputation. Furthermore, our results indicate that although fake news has a smaller effect on first-order judgments, the latter adapt to second-order judgments and thereby affect behavioral intentions. This article contributes, first, to the micro-cognitive perspective on reputation formation by taking the first step in developing a comprehensive understanding of the intricate impact of fake news on reputation and behavioral intentions. Second, this article contributes to our understanding of the role of a good prior reputation as a buffer or a burden.

Fake social media news and distorted campaign detection framework using sentiment analysis & machine learning

Social networking platforms have become one of the most engaging portals on the Internet, enabling global users to express views, share news and campaigns, or simply exchange information. Yet there is an increasing number of fake and spam profiles spreading and disseminating fake information. There have been several conscious attempts to determine and distinguish genuine news from fake campaigns, which spread malicious disinformation among social network users. Manual verification of the huge volume of posts and news disseminated via social media is not feasible and humanly impossible. To overcome the issue, this research presents a framework to use sentiment analysis based on emotions to investigate news, posts, and opinions on social media. The proposed model computes the sentiment score of content-based entities to detect fake or spam and detect Bot accounts. The authors also present an investigation of fake news campaigns and their impact using a machine learning algorithm with highly accurate results as compared to other similar methods. The results presented an accuracy of 99.68 %, which is significantly higher as compared to other methodologies delivering lower accuracy.

Associations between news coverage, social media discussions, and search trends about celebrity deaths, screening, and other colorectal cancer-related events

ObjectiveColorectal cancer (CRC) is the third leading cause of cancer death among both men and women in the United States. CRC-related events may increase media coverage and public attention, boosting awareness and prevention. This study examined associations between several types of CRC events (including unplanned celebrity cancer deaths and planned events like national CRC awareness months, celebrity screening behavior, and screening guideline changes) and news coverage, Twitter discussions, and Google search trends about CRC and CRC screening. MethodsWe analyzed data from U.S. national news media outlets, posts scraped from Twitter, and Google Trends on CRC and CRC screening during a three-year period from 2020 to 2022. We used burst detection methods to identify temporal spikes in the volume of news, tweets, and search after each CRC-related event. ResultsThere is a high level of heterogeneity in the impact of celebrity CRC events. Celebrity CRC deaths were more likely to precede spikes in news and tweets about CRC overall than CRC screening. Celebrity screening preceded spikes in news and tweets about screening but not searches. Awareness months and screening guideline changes did precede spikes in news, tweets, and searches about screening, but these spikes were inconsistent, not simultaneous, and not as large as those events concerning most prominent public figures. ConclusionsCRC events provide opportunities to increase attention to CRC. Media and public health professionals should actively intervene during CRC events to increase emphasis on CRC screening and evidence-based recommendations.

Platform Dependency in the Big Tech Era: Lessons from Australian Facebook Blackout and Botswana Implications

The surge in social media usage for news consumption has led to a heightened reliance on these platforms for accessing information worldwide. In Botswana, Facebook has become one of the primary channels for news consumption, mirroring trends seen in various nations. However, the 2021 Australian Facebook conflict exposed the risks associated with over-reliance on such platforms for news dissemination. In this paper, we investigate the phenomenon of platform dependency, specifically examining the implications for Botswana, in the wake of the Australian Facebook news blackout. Employing a stratified sampling approach, data was gathered by tracking the volume of news posts shared across 51 selected Facebook pages throughout February 2021. The findings revealed a noteworthy trend, with an average of 5630 news items posted or shared on the platform within four weeks. This marked a growing reliance on Facebook-exclusive pages for news consumption, with minimal engagement observed on alternative websites. We argue that there is a precarious position of pages solely reliant on Facebook for news sharing, especially amidst escalating big data politics and regulatory measures that target Tech Giants. Consequently, these pages face an uncertain future, potentially hampering access to vital news reporting and information-sharing to the adaptive population. Currently, in Botswana, there is a notable dearth of academic exploration into the regulation of Big Tech and its potential repercussions for the nation. Thus, this study serves as a cornerstone in pursuing this significant area of inquiry.

Follow the media? News environment and public concern about immigration

AbstractImmigration is a hot topic in Europe, but research on the media effects on public attention to immigration remains limited. We examine how media coverage affects the degree of importance attached to immigration in seven Western European Union member states. Data come from an extensive analysis of claims in printed newspapers, and the Eurobarometer (2002–2009). The continuous sample of news coverage is aggregated into a biannual panel, and we relate these data to citizens’ perceptions of the most important issues in their country 6 months later (lagged). The public consider immigration more important than other policy‐related issues when there is an increase in the volume of news and more political claims on the topic in the media. The media environment appears to be an exogenous actor that can have agenda‐setting effects on public concern about immigration. Our results highlight limitations of both the ‘policy‐gap’ thesis and thermostatic models of policy making.

News intensity and asset returns: the case of currency volatility

ABSTRACT There are both theoretical reasons and empirical evidence for financial markets rewarding investors who put effort into acquiring relevant information. This article shows how a systematic approach of encoding text, ‘semantic fingerprinting’ can be applied to a set of news headlines from The Wall Street Journal to measure the ‘news intensity’ − the volume of relevant news − pertaining to three major currency indices: dollar, pound and euro. In a dataset that spans two decades, we find a persistently positive link between the ‘news intensity’ and the volatility of currency returns, that becomes significantly stronger in times of recession: ‘bad news’ tends to translate into higher volatility.

On the Feasibility of Predicting Volumes of Fake News—The Spanish Case

On the Feasibility of Predicting Volumes of Fake News—The Spanish Case

The First 10 Volumes of SIGLOG News: a retrospective

In July 2014, the first issue of SIGLOG News appeared; so, its official 10th anniversary will be celebrated next summer. In the next pages, I will provide a short retrospective on the first 10 volumes of the publication, that is, on the first 38 issues that appeared so far.

The Application of Random Forest to the Classification of Fake News

Fake News is one of the most widespread phenomenon with significant consequences on our daily life, particularly in the political realm. Due to the increasing use of the internet and social media, it is now much simpler to propagate false information. Therefore, the identification of elusive news is a significant issue that must be addressed, mostly due to obstacles such as the limited number of benchmark datasets and the volume of news produced per second. This study suggested using comparative data analysis based on random forest machine learning algorithm to identify bogus 4news. In this study the size of the whole dataset is 20.761 fake news record, whereas the size of it is 4.345 records. The first step in the data preparation process is to remove any unnecessary special characters, numbers, English letters, and whitespace. Before implementing the proposed classification algorithms, the most prevalent feature extraction approach (TF-IDF) is used. The data indicate that the highest level of accuracy attained was 88.24%.

Apocalypse now or later? Nuclear war risk perceptions mirroring media coverage and emotional tone shifts in Italian news

Abstract This study delves into the progression of nuclear war risk perceptions during the initial 6 months of the Ukraine war. It particularly investigated the influence of Italian media coverage changes and the affective tone of war representation. Utilizing a mixed-methods approach, two separate yet interconnected studies were conducted. The first study employed web scraping and keyword selection techniques to assess emotional language and quantify war-related content in the headlines of Italian online newspapers from March to July 2022. Results demonstrated a linear decrease in war-related news and an emotional shift, with a significant decrease in fear and an increase in joy noted between March and May. The second study examined nuclear war risk perceptions at an individual level, surveying a panel of 397 Italians at three distinct points during the same time frame. The findings revealed a similarity between the media’s affective tone and individuals’ affective risk perceptions. Analytic risk perception, in contrast, showed a linear decrease that matched the decline in war-related news volume. The study found preexisting individual differences, among women and older participants, to be significant determinants in shaping risk perception evolution. These groups exhibited higher initial risk perceptions and more resistance to change as the scenario unfolded. This research contributes to the existing body of work that underscores the media’s role in influencing risk perceptions by illuminating the relationship between media representation of the Ukraine war and individual-level affective risk perception. Furthermore, it highlights individual differences as significant moderators of risk perception change during a crisis.

Issue Introduction | Decoding the Echo Chamber: The Proliferation and Impact of Fake News in Arab Media Post-Arab Spring

In our information-driven society, individuals increasingly consume news instantly via online platforms, including news websites, social media, blogs, and forums. In particular, social media platforms have become fertile ground for the proliferation of fake news, where false information is produced and widely disseminated, often from untraceable IP addresses (Shae and Tsai 2019). These platforms serve as near-perfect conduits for spreading news to a vast audience, given their ability to attract followers. Consequently, the emergence of viral content or 'hotspots' has facilitated the rampant spread of fake news in cyberspace (Dice 2017; Shae and Tsai 2019). This trend has significant implications for global media industries. News editors who receive information via social media often have limited capacity to verify its authenticity, as they might with traditional sources. The task of distinguishing real news from fake becomes increasingly intricate and demanding in the highly competitive media landscape. Various media platforms have started to select and publish posts, comments, and articles from an array of sources without sufficient verification (Wilding and Fray 2018). As a result, assessing the credibility of information has become immensely challenging due to the sheer volume of news, the fragmentation of sources, and the prevalence of biased social cues.

A novel approach to portfolio selection using news volume and sentiment

AbstractIn this study, we develop a novel approach to portfolio diversification by integrating information on news volume and sentiment with the k‐nearest neighbors (kNN) algorithm. Our empirical analysis indicates that high news volume contributes to portfolio risk, whereas news sentiment contributes to portfolio return. Based on these findings, we propose a kNN algorithm for portfolio selection. Our in‐sample and out‐of‐sample tests suggest that the proposed kNN portfolio selection approach outperforms the benchmark index portfolio. Overall, we show that incorporating news volume and sentiment into portfolio selection can enhance portfolio performance by improving returns and reducing risk.

The Spanish population’s interest in climate change based on Internet searches

The climate crisis is one of the most important global problems facing humanity. Analyzing the search for information on climate change (CC) on the internet can be a predictor of public interest in this problem and, therefore, of the degree of concern exhibited by citizens. This study analyzes the interest in CC among the Spanish population and identifies some variables that may influence this interest. The methodology involves the collection and analysis of data obtained from SEMrush and Google Analytics. We analyzed the search trends of four key descriptors related to CC (“climate change,” “global warming,” “climate emergency” and “greenhouse effect”) during two periods of time, and the relationship between these searches and three relational variables (volume of news in the media, occurrence of extreme weather events and CC-related events). The results indicate that the Spanish population’s interest in CC via the Internet has increased in recent years and is directly influenced by variables such as media coverage of CC, events related to CC, and social pressure exerted by social movements for CC. Some proposals are discussed and presented in relation to the concern for this problem.

Key News Event Detection and Event Context Using Graphic Convolution, Clustering, and Summarizing Methods

The detection of key events and identification of the events’ context have been widely studied to detect key events from large volumes of online news and identify trends in such events. In this paper, we propose a Key News Event Detection and Context Method based on graphic convolving, clustering, and summarizing methods. Our method has three main contributions: (1) We propose the use of position vectors as time-embedding feature representations and concatenate semantic and time-embedding features as node features of the graph to distinguish different nodes of the graph. Additionally, a temporal nonlinear function was constructed using time embedding to objectively describe the effect of time on the degree of association between nodes. (2) We update the graph nodes using a graph convolutional neural network to extract deep semantic information about individual nodes of a high-quality phrase graph, thereby improving the clustering capability of graph-based key event detection. (3) We apply a summary generation algorithm to a subset of news data for each key event. Lastly, we validated the effectiveness of our proposed method by applying it to the 2014 Ebola dataset. The experimental results indicate that our proposed method can effectively detect key events from news documents with high precision and completeness while naturally generating the event context of key events, as compared to EvMine and other existing methods.

Characterizing Asians in violence: a comparative analysis of English- and Chinese-language media’s crime news coverage during the pandemic

ABSTRACT This study examined variances in crime news involving Asian perpetrators and Asian victims in a mainstream English-language daily USA Today, and an ethnic newspaper Qiao Bao. Our quantitative content analysis focused on (a) the volume of crime stories published about Asians (as perpetrators and victims respectively), and (b) the news frames used to cover crime incidents involving Asians (as perpetrators and victims respectively). Our results revealed that relative to USA Today, the Chinese-language newspaper Qiao Bao not only reported more violent incidents involving Asians, but also placed less emphasis on thematic frames when covering violent incidents about Asians. On the other hand, the English-language newspaper USA Today published more articles emphasizing the consequences of violence when the crime incidents involved Asians (as either offenders or victims). We theorize that these differences arise due to the nature of ethnic news, as well as structural differences between the two outlets. Situated within the COVID-19 context, this study also found that the volume of crime news about Asian victims significantly grew during the pandemic in Qiao Bao’s coverage but remained unchanged in the USA Today, indicating that Asian victims are more newsworthy to ethnic media and their readers.

Investment and Risk Management with Online News and Heterogeneous Networks

Stock price movements in financial markets are influenced by large volumes of news from diverse sources on the web, e.g., online news outlets, blogs, social media. Extracting useful information from online news for financial tasks, e.g., forecasting stock returns or risks, is, however, challenging due to the low signal-to-noise ratios of such online information. Assessing the relevance of each news article to the price movements of individual stocks is also difficult, even for human experts. In this article, we propose the Guided Global-Local Attention-based Multimodal Heterogeneous Network (GLAM) model, which comprises novel attention-based mechanisms for multimodal sequential and graph encoding, a guided learning strategy, and a multitask training objective. GLAM uses multimodal information, heterogeneous relationships between companies and leverages significant local responses of individual stock prices to online news to extract useful information from diverse global online news relevant to individual stocks for multiple forecasting tasks. Our extensive experiments with multiple datasets show that GLAM outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management application case-studies.

Online disinformation – a multidimensional challenge

Along with other forms of hybrid threat, the phenomenon of disinformation is causing increasing concern to national governments, and also to the international community. The exposure of citizens to widespread disinformation represents a major challenge for civil society and authorities, for researchers and journalists. Open democratic societies depend on public debates that allow well-informed citizens to express their willingness through free and fair political processes. The media plays an essential role in providing information that enables citizens to form their own opinions and to engage effectively and actively in the life of democratic society. In the European Union, democracy is based on the existence of free and independent media. Facilitated access to diverse, quality information can increase participation in democratic processes, giving everyone the opportunity to get involved. Today, the Internet has greatly increased not only the volume and variety of news to that citizens have access, but has also profoundly changed the way citizens access and manage news. In this article, we will briefly present the European approach to the phenomenon of disinformation, widely reflected especially in the research carried out under the auspices of the European authorities, but we will also review the main studies available on this subject.

Sentiment Analysis for Fake News Detection in Online Media Networks: A survey, fusion techniques and quality metrics

The development of Online media sites in recent years has led to the spread of content sharing like commercial advertisements, political news, celebrity news, and so on. Various social media applications, such as Facebook, Instagram, and Twitter, have been impacted by fake news. Due to the easier access and rapid expansion of data through online media platforms, distinguishing between fake and real data has become difficult. The massive volume of news transmitted over online media portals makes manual verification impractical, which has prompted the development and deployment of automated methods for detecting fake news. Given the clear dangers of misleading and deception, fake news study has seen an increase in efforts that employ machine learning approaches, and sentiment analysis. In this study, we review the many implementations of sentiment analysis and machine learning methodologies in the fake news detection, as well as the most pressing difficulties and future research prospects.

Reconstructing social sensitivity from evolution of content volume in Twitter.

We set up a simple mathematical model for the dynamics of public interest in terms of media coverage and social interactions. We test the model on a series of events related to violence in the US during 2020, using the volume of tweets and retweets as a proxy of public interest, and the volume of news as a proxy of media coverage. The model successfully fits the data and allows inferring a measure of social sensibility that correlates with human mobility data. These findings suggest the basic ingredients and mechanisms that regulate social responses capable of igniting social mobilizations.