News Headlines Research Articles

Susceptibility to online misinformation: A systematic meta-analysis of demographic and psychological factors

Nearly five billion people use and receive news through social media and there is widespread concern about the negative consequences of misinformation on social media (e.g., election interference, vaccine hesitancy). Despite a burgeoning body of research on misinformation, it remains largely unclear who is susceptible to misinformation and why. To address this, we conducted a systematic individual participant data meta-analysis covering 256,337 unique choices made by 11,561 US-based participants across 31 experiments. Our meta-analysis reveals the impact of key demographic and psychological factors on online misinformation veracity judgments. We also disentangle the ability to discern between true and false news (discrimination ability) from response bias, that is, the tendency to label news as either true (true-news bias) or false (false-news bias). Across all studies, participants were well above-chance accurate for both true (68.51%) and false (67.24%) news headlines. We find that older age, higher analytical thinking skills, and identifying as a Democrat are associated with higher discrimination ability. Additionally, older age and higher analytical thinking skills are associated with a false-news bias (caution). In contrast, ideological congruency (alignment of participants' ideology with news), motivated reflection (higher analytical thinking skills being associated with a greater congruency effect), and self-reported familiarity with news are associated with a true-news bias (naïvety). We also find that experiments on MTurk show higher discrimination ability than those on Lucid. Displaying sources alongside news headlines is associated with improved discrimination ability, with Republicans benefiting more from source display. Our results provide critical insights that can help inform the design of targeted interventions.

Partisan belief in new misinformation is resistant to accuracy incentives

Abstract One explanation for why people accept ideologically welcome misinformation is that they are insincere. Consistent with the insincerity hypothesis, past experiments have demonstrated that bias in the veracity assessment of publicly reported statistics and debunked news headlines often diminishes considerably when accuracy is incentivized. Many statements encountered online, however, constitute previously unseen claims that are difficult to evaluate the veracity of. We hypothesize that when confronted with unfamiliar content, unsure partisans will form sincere beliefs that are ideologically aligned. Across three experimental studies, 1,344 conservative and liberal US participants assessed the veracity of 20 politically sensitive statements that either confirmed or contradicted social science evidence only known to experts. As hypothesized, analyses show that incentives failed to correct most ideological differences in the perceived veracity of statements. 66 to 78\% of partisan differences in accuracy assessment persisted even when monetary stakes were raised beyond levels in prior studies. Participants displayed a surprising degree of confidence in their erroneous beliefs, as bias was not reduced when participants could safely avoid rating statements they were unsure about, without monetary loss. These findings suggest limits to the ability of disciplining interventions to reduce the expression of false statements, because many of the targeted individuals sincerely believe them to be true.

Applications of machine learning in quantitative trading

Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.

Correction: Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

Correction: Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

Inoculation and accuracy prompting increase accuracy discernment in combination but not alone.

Misinformation is a major focus of intervention efforts. Psychological inoculation-an intervention intended to help people identify manipulation techniques-is being adopted at scale around the globe. Yet the efficacy of this approach for increasing belief accuracy remains unclear, as prior work uses synthetic materials that do not contain claims of truth. To address this issue, we conducted five studies with 7,286 online participants using a set of news headlines based on real-world true/false content in which we systematically varied the presence or absence of emotional manipulation. Although an emotional manipulation inoculation did help participants identify emotional manipulation, there was no improvement in participants' ability to tell truth from falsehood. However, when the inoculation was paired with an intervention that draws people's attention to accuracy, the combined intervention did successfully improve truth discernment (by increasing belief in true content). These results provide evidence for synergy between popular misinformation interventions.

Translation Evaluation of Social Media Auto-Translate Feature toward News Headline

Machine translation has become an everyday part of society's news consumption patterns. Unfortunately, important news content is poorly mediated by social media, especially on the output of the translation system feature on the site. Therefore, further research is needed on the evaluation of the auto-translate system on social media. This study focuses on the results of linguistic evaluation on the output of social media machine translation in translating news headlines. The data were taken from news coverage by the Reuters channel in the period of September 1st–7th, 2023 and from social media such as Facebook and Twitter. The translation evaluation used linguistic elements consisting of orthography, morphology, lexical, semantic, and syntax. This study found various kinds of linguistic non-equivalence. Orthographic non-equivalence includes punctuation, capitalization, and apostrophes. Morphological non-equivalence in the two systems was found in number concordance, inflection, derivation, and compounding. Lexical errors occur in both systems in the form of no correspondence and missing target words, as well as untranslated words and proper noun. In terms of semantics, both systems had difficulties with polysemy, homonymy, and expression. Finally, syntactic errors occur with missing articles, prepositions, and inappropriate arrangements of syntactic elements.

Unveiling Market Sentiments: Finbert-Powered Analysis of Stock News Headlines

This document Sentiment analysis based on news and headlines is a big part of financial markets. Through the utilization of Hugging Face and FinBERT-a specialized model for financial sentiment analysis-and advanced natural language processing techniques, this study makes use of the flexibility of Hugging Face. This study concentrates on pre-processing techniques and the implementation of a model, emphasizing the critical evaluation and correction of inherent biases in sentiment analysis. Results of the experiment show that FinBERT is effective in addressing and reducing biases while extracting diverse sentiments from stock market headlines. This study emphasizes the importance of bias-conscious sentiment analysis for making more informed decisions in financial markets. It highlights the importance of advanced natural language processing models (NLP) like FinBERT and powerful frameworks like Hugging Face. Key Words: optics, Financial Sentiment Analysis, FinBERT, Hugging Face, Natural Language Processing (NLP), Bias Mitigation, Stock Market, Sentiment Analysis, Machine Learning, News Headlines, Investment Decision-making, Data Pre-processing, Neural Networks, Market Sentiments, Text Classification, NLP Frameworks

The Scramble for the West? Debunking the Myths around Migration from Africa and the Global South

Irregular migrants’ flows have, for several years now, been making the news headlines and are at the core of political debates. In a context of polycrisis—characterized by the post COVID-19 pandemic, climate change and its devastating impacts; the disruption of labor markets caused by the fourth industrial revolution and artificial intelligence; or geopolitical tensions in different parts of the globe that threaten the livelihoods of many communities—the desire to move from the developing and emerging world to the West can increase.

The role of news sentiment in salmon price prediction using deep learning

This paper employs deep learning models and sentiment analysis to predict salmon spot prices. Our data includes historical price data and sentiment scores from 2018 to 2022. We extract sentiment scores from salmon-related news headlines by using FinBERT and TextBlob. We begin with price prediction using only historical price data and then introduce sentiment scores to improve the prediction accuracy of deep learning models. We find that the prediction performance of deep learning models outperforms traditional prediction methods in the salmon market. Our primary hybrid CNN-LSTM model outperforms other deep learning models and traditional models. Additionally, deep learning models incorporating sentiment scores exhibit reduced prediction errors. Our findings confirm the value of sentiment information in improving forecasting performance. These findings highlight the effectiveness and robustness of our CNN-LSTM model combined with sentiment analysis for price prediction in the salmon market.

Cross-modal scenario generation for stock price forecasting using Wasserstein GAN and GCN

The forecasting of stock price has always been a difficult problem, as the various inputs like company performance, technical innovation, political factors are intricate and often assessed with uncertainty. While most of the existing studies belong to deterministic forecast that focused on either the trend or exact value of future stock price, the forecasted results contain little uncertain information, thus bringing certain risks to investors. In this study, an effective scenario generation method based on wasserstein generative adversarial network (WGAN) is proposed for stock price forecasting, which can characterize the temporally and spatially correlations among different stocks, so as to help investors make proper investment strategies in complicated market environments. To the best of our knowledge, the scenario generation of stock price has been rarely studied by existing publications. Specifically, considering the inherent correlation among stocks, graph convolutional network (GCN) is used to capture the features of related stocks, whereafter the features of target stocks and related stocks are combined. In terms of feature fusion, this paper further uses a pre-trained model to obtain the key information of news headlines as text features, and fully integrates them with historical data through the attention mechanism, thus improving the overall performance of scenario generation. In summary, the proposed model can generate promising scenarios for future stock prices over time based on cross-modal information like historical data and news headlines. Numerical experiments show that our method outperforms other baseline methods in terms of root-mean-square error and average absolute percentage error (the average of several scenarios). For each part of the method, stock correlation is the most helpful for the results, followed by cross-modal information, with scenario generation contributing less to the results. Besides, experiments on a real-life portfolio selection problem also demonstrate that our method brings the highest returns, which proves its effectiveness in practical application.

Deep Learning Based Prediction Model for Crude Palm Oil Prices Using News Sentiment Analysis with Sliding Window

Crude palm oil (CPO) price prediction plays an important role in agricultural economic development. Various economics and agricultural related factors have been used to predict CPO prices. Nevertheless, understanding news sentiment features will also be important in CPO price prediction. This paper proposes a CPO price prediction model to help the plantation organizations in the palm oil sector to successfully anticipate CPO price fluctuations and manage the resources more effectively. The CPO price behavior is non-linear in nature, and thus prediction is very difficult. In this paper, an improved version of recurrent network, Long Short-Term Memory (LSTM) based CPO price prediction model with news sentiment is used to produce an enhanced prediction model. The findings of this study show that the LSTM based forecasting model with news headline sentiment using a six-month sliding window produced the best result in forecasting the CPO price movement compared to other sliding window sizes.

Examining the influence of mass media on academic performance in basic three students at Senase Methodist Primary 'A' School

The study examined the influence of mass media on the academic performance of pupils among basic Three pupils of Senase Methodist primary “A” school. The study employed the descriptive survey design (cross-sectional) with a quantitative approach. The study targeted Basic Three pupils of Senase Methodist Primary “A” School. A questionnaire on students’ usage of mass media was used in the conduct of the study. Means and standard were used to analyse the data to answer the research questions. The findings of the study revealed that Senase Methodist Primary “A” School students use mass media sites for a number of purposes. Among some of these purposes include academic usage of social media, using social media sites for the purpose of socialization, entertainment usage of social media as well as using social media for the purpose of reading headline news. Based on the findings, it was recommended that counsellors and teachers should guide and educate students on the dangers of being addicted to social media usage, since too much time spent on social media sites could affect students’ academic performance.

Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

The study aims to investigate the ideological manipulation of language within news media headlines regarding the killing of Palestinian journalist Shireen Abu Akleh. This study employs Van Dijk’s socio-cognitive model of Critical Discourse Analysis to conduct a comparative qualitative analysis of 100 Arabic and 100 English news headlines, exploring linguistic features at both microstructure and macrostructure levels. The analysis delves into identity construction for the victim and perpetrator, revealing the discursive linguistic manifestations of underlying ideologies in two distinct linguistic and cultural contexts. The microstructure analysis reveals the preference of Arabic headlines for active voice structure, rhetorical devices of numeration, emphatic language, and metaphors. On the other hand, English headlines preferred the passive voice structure and the rhetorical device of litotes. The macrostructure analysis unravels the cognitive interplay between news media outlets and their target audience, demonstrating how selective headlines significantly impact public opinion. It shapes their comprehension and interpretation, particularly within the broader context of the Israeli-Palestinian conflict. Ultimately, this research highlights the dynamic interplay between language, ideology, and power in news media, offering key recommendations for understanding their comparative impacts.

The Vague Expressions in English News Headlines and Film Works

The Vague Expressions in English News Headlines and Film Works

Framing of the Covid-19 Infodemic on Topics and Tones of News Headlines in Indonesian and Malaysian Online Newspapers

During the COVID-19 pandemic, the mass media was critical in conveying information about public health concerns and initiatives by the government aimed at mitigating the pandemic. This study used content analysis to determine how the COVID-19 news was framed in Indonesian and Malaysian media published between January and March 2021. This study examined the Republika and The Star newspapers. According to the findings of the data analysis, the results of this research found five general topics in both newspapers: public health, government policy, economics, politics, and culture and religion. The Republika newspaper publishes the most articles on public health, whereas The Star newspaper publishes the most articles on government policy, economy, politics, culture, and religion. However, public health and government policy are more popular topics in both countries. Meanwhile, based on the two newspapers, there are five types of news tones: solutions, fears, impacts, suggestions, and conflicts. A fearful tone is prevalent in Republika news, while the other four news tones may be found in The Star. The high prevalence of news with a fearful tone in the Republika newspaper is due to the high death rate caused by COVID-19, as well as the emergence of a new variant of COVID-19, even though news in Malaysia is widely broadcast using a solution tone and positive messages to readers.

A Health Media Literacy Intervention Increases Skepticism of Both Inaccurate and Accurate Cancer News Among U.S. Adults.

Inaccurate cancer news can have adverse effects on patients and families. One potential way to minimize this is through media literacy training-ideally, training tailored specifically to the evaluation of health-related media coverage. We test whether an abbreviated health-focused media literacy intervention improves accuracy discernment or sharing discernment for cancer news headlines and also examine how these outcomes compare to the effects of a generic media literacy intervention. We employ a survey experiment conducted using a nationally representative sample of Americans (N = 1,200). Respondents were assigned to either a health-focused media literacy intervention, a previously tested generic media literacy intervention, or the control. They were also randomly assigned to rate either perceived accuracy of headlines or sharing intentions. Intervention effects on accurate and inaccurate headline ratings were tested using OLS regressions at the item-response level, with standard errors clustered on the respondent and with headline fixed effects. We find that the health-focused media literacy intervention increased skepticism of both inaccurate (a 5.6% decrease in endorsement, 95% CI [0.1%, 10.7%]) and accurate (a 7.6% decrease, 95% CI [2.4%, 12.8%]) news headlines, and accordingly did not improve discernment between the two. The health-focused media literacy intervention also did not significantly improve sharing discernment. Meanwhile, the generic media literacy intervention had little effect on perceived accuracy outcomes, but did significantly improve sharing discernment. These results suggest further intervention development and refinement are needed before scaling up similarly targeted health information literacy tools, particularly focusing on building trust in legitimate sources and accurate content.

Abstract A164: Rural and religious differences in online news outlet headlines about HPV and HPV vaccines in Utah

Abstract Purpose: To evaluate the penetration of online HPV vaccine information in Utah, where HPV vaccination and completion rates are low, and uncover potential disparities by religiosity and rurality in the types of language used by news headlines in the social media ecosystem. Methods: We described the language used in local news outlets' headlines about HPV and HPV vaccines. We included headlines posted on Facebook from 23 news outlets within 6 counties (including high and low vaccination, rurality, and religiosity) from 2011-2023 to represent the public online discourse about HPV vaccination. We searched each news outlet’s Facebook page for eligible posts using 11 key terms (e.g., HPV, HPV vaccination) that were identified through literature review and refined with content experts, resulting in N=851 headlines. After removing duplicates, there were a total of n=531 unique headlines for analysis. Area-level (county) measures of rurality (2013 Rural and Urban Continuum Codes) and religiosity (Public Religion Research Institute 2020 Census) were coded for each post title based on the originating news outlet. We analyzed the language used in each headline. Differences in the valence of online discourse about HPV and HPV vaccination by rurality and religiosity were compared using summary statistics and t-tests. Linguistic analyses were conducted using Linguistic Inquiry Word Count and Stata 18. Results: Overall, urban areas were less religious than rural areas (28% unaffiliated in urban areas vs. 13-26% unaffiliated in rural areas, p<0.001). There were more headlines about HPV and HPV vaccination from news outlets in urban than rural areas (69.7% vs. 30.3%). Nearly 70% of HPV and HPV vaccine headlines came from the least religious areas—counties whose religiously unaffiliated populations ranged from 28%-32%. News outlets differed in how they disseminated information about HPV and HPV vaccines by rurality and religiosity. On average, news outlets in urban areas used more positive language than in rural areas (mean 2.04 vs. 1.28 words, p<0.01). Rural news outlets used more emotion-laden terms than those in urban areas (mean 1.2 vs. 0.6 words, p<0.01) and negative emotion was more prevalent among rural than urban headlines (mean 0.73 vs. 0.42 words, p=0.02). These observations were mirrored for areas with higher religiously unaffiliated populations demonstrating titles with more positive words (mean 2.05 vs. 1.27, p<0.01), less emotional words (mean 0.63 vs. 1.17 words, p<0.01), and less negative language (mean 0.42 vs. 0.73 words, p=0.01) compared to headlines in areas with lower proportions of religiously unaffiliated, respectively. Conclusions: The penetration of HPV and HPV vaccine information in rural and religious areas is less positive, more emotionally charged, and uses more negative emotional language than in urban areas. This may, in part, perpetuate the low HPV vaccine uptake in these regions. Citation Format: Echo L. Warner, Ida Tovar, Kaila Christini, Stephen Gubler, Becca Slagle, Jennifer Smith, Michelle Litchman, Wendy Birmingham, Deanna Kepka. Rural and religious differences in online news outlet headlines about HPV and HPV vaccines in Utah [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A164.

Populist Leaders as Gatekeepers: André Ventura Uses News to Legitimize the Discourse

This study explores the role of populist leaders as gatekeepers on social media, seeking to understand how André Ventura, president of Chega!, uses news to legitimize his political discourse. The methodology involved collecting 90 tweets containing legacy media news features, posted by Ventura on the social media platform X. These tweets cover key political events such as the resignation of Portugal’s Prime Minister, the dissolution of the Portuguese Parliament, and European elections. Quantitative analysis using Voyant Tools identified key terms related to Ventura’s ideological stance, while Critical Discourse Analysis (CDA) examined how these terms support his political narrative. The findings reveal a strategic use of news to promote themes like nationalism, immigration control, corruption and social dichotomy between “us” and “them”. Ventura’s tweets leverage news headlines to enhance his persuasive appeal, acting as heuristic shortcuts to reinforce his political messages. This study highlights the relevance of understanding social media’s role in promoting populism and suggests avenues for future research, including comparative analyses of other populist leaders and the impact of these narratives on voter behavior and perceptions.

Sarcasm Detection on Social Media: Addressing the Issues of Short and Long Texts Using Machine Learning and Deep Learning Approaches

Sarcasm is a linguistic term that conveys a meaning that is different from what is meant to be said, frequently used to mock, taunt, or convey disdain. Sarcasm is a complicated social phenomenon that can be recognized by its tone of voice, exaggeration, or context. Because sarcastic discourse is nuanced, detecting sarcasm in Natural Language Processing (NLP) has become a major difficulty. This paper thoroughly examines the tradition machine learning approaches like SVM, Naive Bayes and Random Forest well as advanced deep learning methods such as RNN, LSTM, and transformer based models, like BERT, that has demonstrated superior performance used for sarcasm detection, and also the paper offers more sophisticated data preprocessing techniques that comprise several stages, each focusing on a different facet of the fragmented and informal character of the social media material. It focuses mainly on short and long texts that may be found in news headlines and social media sites like Facebook, Instagram, X (formerly known as Twitter), and others. The paper also explores various challenges like cultural and linguistic barriers, and increased use of audio and visual sarcastic content on social media. At last paper concludes with possible guidelines for future works including the development of real-time, multilingual systems to examining holistic strategies that can encompass the complexity of sarcasm in multimodal communications. Key Words: sarcasm, NLP, social media, machine learning, deep learning

The role of personality traits and online behavior in belief in fake news

The current study examines how careless online behavior and personality traits are related to the detection of fake news. We tested the relationships among accurately distinguishing between fake and real news headlines, careless online behavioral tendencies, and the HEXACO and dark triad personality traits. Poorer discernment between fake and real news headlines was associated with greater careless behavior online (i.e., greater online disinhibition, greater risky online behavior, greater engagement with strangers online, and less suspicion of others’ intentions online), as well as lower Conscientiousness, Openness, and Honesty-Humility, and greater dark triad traits. Implications for the literature as well as potential interventions to reduce susceptibility to misinformation and fake news are discussed.