Analysis of Online News Media Sentiment Toward the Free Nutrisious Meals Program Policy

Media Sentiments on Stakeholders and Daily Abnormal Returns during COVID-19 Pandemic: Early Evidence from the US

As the media have continued to pay increasing attention to pig epidemic events, some local pig epidemic events may have a large degree of negative impact on the pork market and the whole pig industry chain, leading to pork price fluctuations. Strengthening pig epidemic control, monitoring media reporting sentiment, and stabilizing pork price fluctuations are important measures to improve the economy and people’s livelihood. This paper sets out to identify the relationship between the negative media sentiment about the pig epidemic and the market risk of pork prices within a setting with pig epidemic risk. Based on the provincial panel data of China from January 2011 to December 2022, this paper uses the spatial panel Durbin model to investigate the impact of negative media sentiment about the pig epidemic on pork price fluctuations from the perspective of local and spillover effects, and further discusses the mechanism of consumer sentiment. The empirical results show that: (1) The negative media sentiment about the pig epidemic significantly exacerbates pork price fluctuations, and there is a single threshold effect, which is weakened after crossing the threshold value. (2) The negative media sentiment about the pig epidemic has a significant positive spillover effect on pork price fluctuations, showing the characteristics of “being a neighbor”. The spatial spillover effect shows a significant spatial attenuation feature and an inverted U-shaped change with the inflection point at 1400 km. (3) The effect is related to the heterogeneity of media reputation. The local aggravation effect of local media’s negative sentiment on pork price fluctuations is greater than that of central media and information network platforms. In terms of the spatial spillover effect, the negative sentiment of the information network platforms has the strongest effect on the aggravation of pork price fluctuations in neighboring regions. (4) The mechanism study finds that the negative media sentiment about the pig epidemic positively affects pork price fluctuations through the path of “consumer sentiment”. Therefore, this research recommends that the government department should strengthen the supervision of media sentiment about the pig epidemic and reasonably guide consumer sentiment to stabilize the pork market.

General Background: The Russia-Ukraine conflict has emerged as a pivotal global issue, significantly reshaping the geopolitical landscape. Specific Background: This study examines how mass media outlets, particularly BBC Indonesia and CNN Indonesia, frame their coverage of the conflict, emphasizing the role of media narratives in public perception. Knowledge Gap: While existing research highlights media influence in conflict reporting, there is limited comparative analysis focusing on Indonesian media and their unique framing techniques. Aims: The research aims to explore the differences in narrative construction and coverage strategies employed by BBC Indonesia and CNN Indonesia regarding the Russia-Ukraine conflict. Results: Through a systematic review of news reports, the findings reveal distinct approaches in the two media outlets, characterized by variations in perspective, source selection, and information presentation styles, despite a shared commitment to accuracy. Novelty: This study contributes new insights into the comparative framing of international conflicts by Indonesian media, underscoring the importance of contextual factors in shaping news narratives. Implications: The results suggest that understanding these framing differences is crucial for evaluating media influence on public opinion and discourse surrounding global conflicts, potentially guiding future research and media literacy initiatives in Indonesia and beyond. Highlights: Comparative Analysis: The study fills a gap in research by comparing the framing techniques of two prominent Indonesian media outlets, revealing unique narrative constructions. Diverse Approaches: Findings indicate that BBC Indonesia and CNN Indonesia utilize different perspectives and source selections, affecting how the conflict is portrayed. Impact on Public Discourse: Understanding these framing differences is essential for assessing media influence on public opinion regarding international conflicts. Keywords - Russia-Ukraine conflict, media framing, BBC Indonesia, CNN Indonesia, public perception

PurposeThis study investigates the relationship between earnings call sentiment and subsequent media coverage sentiment. Examining these synergistic effects between executive communication style and resulting news narratives provides novel insights. The unscripted qualitative discussions in earnings calls establish perceptions and outlooks that the media echoes in later coverage. Understanding these intricate connections between information channels aids communication experts and market analysts in shaping strategic messaging and predicting market impacts. In addition, the link with the stock return reaction is revisited, and this study shows that the effects on stock returns driven by news information are moderated by earnings call sentiments.Design/methodology/approachThis study analyzes the interplay between earnings call sentiments and subsequent news sentiments for 30 S&P 500 companies from 2012 to 2022. Utilizing the FinBERT Natural Language Processing (NLP) model, we extract sentiment scores from earnings call transcripts and corresponding news articles. We apply OLS regression models to examine the relationship between negative earnings call sentiments and subsequent negative news sentiments, as well as their combined impact on stock returns. Control variables include financial metrics such as ROA, ROE, firm size, Market-to-Book ratio and liquidity. The methodology allows for a nuanced exploration of sentiment transfer mechanisms in financial communication and their market implications.FindingsOur study reveals a significant positive correlation between negative sentiment in earnings calls and subsequent negative news sentiment. A 1% increase in negative call sentiment associates with a 0.54% increase in negative news sentiment the following day, supporting Agenda Building and Impression Management hypotheses. We observe a multiplicative effect on stock returns when negative call sentiment coincides with negative news sentiment, supporting signaling theory. Financial metrics like ROE show marginal influence on news sentiment, while others demonstrate insignificant impact. These findings underscore the importance of holistic corporate communication management in mitigating potential negative market reactions.Research limitations/implicationsThis study’s primary limitation is its sample size of 30 S&P 500 companies, potentially limiting generalizability. The use of a single sentiment analysis model (FinBERT) could impact results, warranting comparison with alternative methods. The study’s timeframe (2012–2022) may not capture the most recent market dynamics. Future research could expand the sample size, incorporate additional sentiment analysis techniques and explore longer-term effects. Investigating industry-specific variations and the impact of macroeconomic factors could provide further insights. Additionally, qualitative analysis of earnings call content could complement these quantitative findings, offering a more comprehensive understanding of sentiment transfer mechanisms.Practical implicationsThis study offers insights for corporate communicators, investor relations professionals and financial analysts. The strong correlation between earnings call sentiment and subsequent news sentiment emphasizes the need for management of corporate messaging during these calls. Companies should be aware that negative sentiments expressed in earnings calls may amplify through news coverage, potentially impacting stock performance. Investors and analysts should consider both earnings call and news sentiments when evaluating market reactions. For regulators, these findings highlight the importance of monitoring information dissemination practices to ensure market fairness. Overall, the study underscores the significance of a holistic approach to financial communication strategy.Social implicationsThis research highlights the interconnected nature of corporate communication and media narratives, emphasizing social responsibility of both corporations and news outlets. The findings suggest that negative corporate messaging can perpetuate and amplify through news coverage, potentially affecting public perception and investor sentiment. This underscores the need for transparent and ethical communication practices in the business world. The study also raises awareness about the potential manipulation of public opinion through carefully crafted corporate narratives. It encourages stakeholders to critically evaluate both corporate communications and subsequent media coverage, promoting a more informed and discerning society in the context of financial information dissemination.Originality/valueThis study uniquely explores the interplay between earnings call sentiments and subsequent news sentiments, addressing a significant gap in financial communication research. By examining the sentiment transfer mechanism from corporate messaging to media narratives, it provides novel insights into information dissemination in financial markets. The research demonstrates how negative sentiments in earnings calls can amplify through news coverage, offering valuable implications for corporate communication strategies. This multifaceted analysis contributes to a deeper understanding of the complex relationships between corporate communication, media coverage and market behavior.

By examining negative sentiment toward recent migrants among local residents in Hong Kong, this study fills a research gap in understanding group relations between migrants and local residents in post-colonial societies. We suggest that negative sentiments toward recent migrants among local residents in Hong Kong are the result of the society's post-colonial development, which has fostered a local identity and defined a group boundary between residents born in Hong Kong and migrants from the mainland. Linking post-colonial literature with literature on group boundaries, group threats, and scapegoating, we developed four hypotheses to explain the negative sentiments of local residents toward Chinese migrants. Using findings from data collected in 2014, we show that having close friends from mainland China, having higher income, and level of job satisfaction are all related to the level of negative sentiment toward mainland migrants in Hong Kong. Implications of the findings are discussed.

Consumers’ sentiment toward marketing appears to be an area of important concern to both practitioners and researchers since it can be expected to affect their shopping and purchasing activity. The purpose of this study is to examine the sentiment toward marketing of collegiate business students. Specifically, does the sentiment toward marketing differ between business students attending universities that differ in orientation, such as those attending state universities and those attending Jesuit universities affect students’ sentiment toward marketing? The findings suggest that students attending the Jesuit university may possess more negative sentiment toward marketing. The increased emphasis on social justice and social responsibility at the Jesuit university may increase the standards that the students expect from marketers, leading to more negative sentiment toward marketing when businesses do not perform at these standards.

Objectives: The research aims to describe the position of the media in Indonesia in constructing an event using framing analysis. Methods: One of the framing analysis forms existing in communications research is that by Zhongdang Pan and Gerald M. Kosicki, which involves four major structures, namely, syntactic structures, script structures, thematic structures, and rhetorical structures. Framing analysis is a form of qualitative research. It particularly has relevance to the phenomenon of capital city relocation news reporting. Data were collected from online news reports by four media outlets, namely, Metro TV, CNN Indonesia, CNBC Indonesia, and Kompas TV. The news reports analyzed were set within the time frame between April of 2019 and January of 2022. All the online news reports pertaining to the capital city relocation were compared between the four media outlets to draw a conclusion as to how the media outlets positioned themselves in constructing the event under investigation. Results: The findings indicated that each television media outlet presented news on the capital city relocation policy in their respective fashions: Metro TV showed approval, CNBC Indonesia and CNN Indonesia did an inclination towards disapproval, and Kompas TV did a neutral view regarding the capital city relocation policy. Conclusions: The four media that were the object of research show that each media has a different agenda regarding events that occurred in Indonesia, especially the relocation of the capital city. These differences come from differences in motives, vision and mission, ideology, political affiliation and goals.

This research uses GPT to conduct sentiment analysis on customer reviews for biodegradable products. Sentiment analysis uses 4 categories, namely positive, negative, neutral and mixed, then for product improvement this study focuses on negative sentiment by adding negative sentiments from the mixed sentiments. Data collected from Amazon reviews regarding one brand of biodegradable trash bag in several stores. The GPT-4o Mini model was then used to categorize sentiment.The results of sentiment analysis show that most reviews are positive, but there are also many negative sentiments regarding product durability, leakage and price. The model used is able to accurately identify and extract negative sentiment even from a mixed sentiment, thereby providing a more complete understanding of customer dissatisfaction. This research emphasizes the importance of integrating AI-driver sentiment analysis into the marketing process.

Discussion of Dhaliwal, Goodman, Hoffman, and Schwab (2019): Revisiting Tax-Related Reputational Costs

PurposeThis study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.Design/methodology/approachFrom more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.FindingsFirst, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.Originality/valuePerforming sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.

BackgroundAs the spread of the SARS-CoV-2 virus coincided with lockdown measures, it is challenging to distinguish public reactions to lockdowns from responses to COVID-19 itself. Beyond the direct impact on health, lockdowns may have worsened public sentiment toward politics and the economy or even heightened dissatisfaction with health care, imposing a significant cost on both the public and policy makers.ObjectiveThis study aims to analyze the causal effect of COVID-19 lockdown policies on various dimensions of sentiment and uncertainty, using the Italian lockdown of February 2020 as a quasi-experiment. At the time of implementation, communities inside and just outside the lockdown area were equally exposed to COVID-19, enabling a quasi-random distribution of the lockdown. Additionally, both areas had similar socioeconomic and demographic characteristics before the lockdown, suggesting that the delineation of the strict lockdown zone approximates a randomized experiment. This approach allows us to isolate the causal effects of the lockdown on public emotions, distinguishing the impact of the policy itself from changes driven by the virus’s spread.MethodsWe used Twitter data (N=24,261), natural language models, and a difference-in-differences approach to compare changes in sentiment and uncertainty inside (n=1567) and outside (n=22,694) the lockdown areas before and after the lockdown began. By fine-tuning the AlBERTo (Italian BERT optimized) pretrained model, we analyzed emotions expressed in tweets from 1124 unique users. Additionally, we applied dictionary-based methods to categorize tweets into 4 dimensions—economy, health, politics, and lockdown policy—to assess the corresponding emotional reactions. This approach enabled us to measure the direct impact of local policies on public sentiment using geo-referenced social media and can be easily adapted for other policy impact analyses.ResultsOur analysis shows that the lockdown had no significant effect on economic uncertainty (b=0.005, SE 0.007, t125=0.70; P=.48) or negative economic sentiment (b=–0.011, SE 0.0089, t125=–1.32; P=.19). However, it increased uncertainty about health (b=0.036, SE 0.0065, t125=5.55; P<.001) and lockdown policy (b=0.026, SE 0.006, t125=4.47; P<.001), as well as negative sentiment toward politics (b=0.025, SE 0.011, t125=2.33; P=.02), indicating that lockdowns have broad externalities beyond health. Our key findings are confirmed through a series of robustness checks.ConclusionsOur findings reveal that lockdowns have broad externalities extending beyond health. By heightening health concerns and negative political sentiment, policy makers have struggled to secure explicit public support for government measures, which may discourage future leaders from implementing timely stay-at-home policies. These results highlight the need for authorities to leverage such insights to enhance future policies and communication strategies, reducing uncertainty and mitigating social panic.

BackgroundDuring the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices.ObjectiveThe purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors.MethodsThe authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19–related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States.ResultsIn the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues.ConclusionsThe differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises.

&lt;p&gt;&lt;span lang="EN-US"&gt;The Domestic Worker Protection Bill (RUU PPRT) has been a critical issue in Indonesia, yet its legislative process has stagnated for two decades, leading to intense public discourse. This study aims to analyze the sentiment and narrative dynamics of RUU PPRT news coverage in online media, as well as the media's role in shaping public opinion. Employing a Text Mining and Lexicon-Based Sentiment Analysis approach, enhanced with adaptations for the Indonesian lexicon, this research analyzes 387 news articles from three prominent online media outlets (Tempo, Kompas, and VOA News) published between 2004 and 2024. The findings reveal that positive sentiment dominates with 58.1%, followed by negative sentiment at 31.3%, and neutral sentiment at 10.6%. Tempo was identified as the most active media outlet covering this issue. &lt;/span&gt;&lt;span class="ts-alignment-element"&gt;&lt;span lang="EN-US"&gt;These results indicate that the mass media plays a significant role in shaping the pattern of public discourse regarding the PPRT Bill, particularly through the dominance of positive sentiment in its reporting and confirm that lexicon-based sentiment analysis can systematically capture the dynamics of complex socio-political narratives.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

Media sentiment in the stock market affects investors’ behaviors and the operation of the stock market. Meanwhile, the development of data mining and artificial intelligence makes it possible to study massive stock market news on the Internet. In order to study media sentiment in the stock market, this paper collected the stock market news of 42 constituent stocks of China Securities 100 index in 2018 from Sina News. Firstly, we constructed financial news sentiment classifier based on word2vec and support machine vector (SVM). Then, we created three new daily sentiment indexes of individual stock by using the classification results. Finally, linear regression models were built to explore their relationship with the logarithmic trading volume, turnover, yield and volatility of individual stocks. The accuracy of financial news sentiment classifier was 86%, and the regression models could explain the influence of stock market media sentiment on some indicators of stock market to some extent. It was concluded that the financial news sentiment classifier based on word2vec and SVM works well. In addition, choosing the appropriate method to convert the word vector into the text vector has a great impact on the model effect and the simple average method performed better in this paper. At the same time, media sentiment has effects on the stock market. The impacts of negative and positive sentiment on logarithmic trading volume and turnover are positive. The positive impact of neutral sentiment on turnover is weaker than that of positive and negative sentiment. KeywordsMedia sentimentStock marketSentiment analysisWord2vecSupport vector machineLinear regression

This paper discussed the possibility of utilizing a sentiment analysis of online discussions on X platform (which was previously X) as a predictor of cyber defacement attacks. It bridged a serious gap in the literature on cybersecurity, where the focus has been on technical signatures and little consideration has been made on socio-technical antecedents. The hypothesis that spikes of negative public sentiment might be predictive indicators of ideologically motivated cases of defacement was tested in the study. A hybrid sentiment analysis model was used, which incorporates lexicon-based VADER model with machine learning classifiers, such as Naive Bayes and Long Short-Term Memory networks. The data consisted of 503456 posts related to cybersecurity and the data were compared to the verified cases of defacement in repositories like Zone-H using time-series analysis, Pearson correlation, and cross-correlation functions. Findings indicated that negative sentiment only comprised of 8.6% of the posts with the majority being neutral (50.9) and positive (40.5). The temporal analysis showed that there is not a substantial change in negative sentiment, but short bursts of negative sentiment are associated with cybersecurity disclosure. The cross-correlation analysis showed only weak contemporaneous correlation (r ≈ 0.12, lag = 0 days) but no predictive correlation in negative lags. The stacked ensemble model (Naïve Bayes, BiLSTM, ARIMA) was very strong in classification (Accuracy = 0.8568, F1 = 0.8055, ROC-AUC = 0.9116) but mainly it was very sensitive to concurrent or retrospective signals. The research established that aggregate sentiment does not provide predictive information, socio-technical prediction would combat inactive fine-grained and entity-specific signals combined with technical threat knowledge.