Twitter Posts Research Articles

Impact of positive and negative emotions on protest for institutional reform: An analysis of #EndSars Twitter posts

A protest is a collective action undertaken by a group of people to achieve a common goal. Participation in protests is motivated by appraisals of a social condition. The dual pathway model of collective action has been extended to consider both negative and positive emotional paths to protest. This study further shows the prevalence of positive and negative emotions as they differ systematically between the different phases of protest. This study divides the protest period into four phases: before the trigger event, during the trigger event, during the protest, and reform announcement. A sentimental analysis of how positive and negative emotions vary across the four phases of protest was conducted using the Twitter posts of the #EndSARS protest in Nigeria. Findings revealed that both positive and negative emotions co-occur alongside the protest phases. Trust was dominant before the trigger event, fear was dominant during the trigger event, anger was dominant during the protest and joy was dominant following the reform announcement. Findings from this study indicate that fear and anger instigate protest. Anticipation motivates protest and joy is the feeling of satisfaction experienced when the goal of a protest is achieved.

The extent of energy drink marketing on Canadian social media

BackgroundCaffeinated energy drink (CED) consumption among children and adolescents is a growing global public health concern due to its potential to produce adverse effects. CED marketing viewed by children and adolescents contributes to this problem as it increases consumption and favourable attitudes towards these high-caffeine and high-sugar products. This study aimed to describe the social media marketing of CEDs by estimating the frequency of user-generated and company-generated CED marketing and analyzing the marketing techniques used by Canadian CED brands on social media.MethodsCED products and brands were identified using the list of CEDs that received a Temporary Marketing Authorization from Health Canada in June 2021. The data on the frequency, reach and engagement of CED-related posts created by users and Canadian CED brands on Facebook, Instagram, Twitter, Reddit, Tumblr, and YouTube were licensed from Brandwatch for 2020–2021. A content analysis was conducted to assess the marketing techniques used in Canadian CED company-generated posts using a coding manual.ResultsA total of 72 Canadian CED products were identified. Overall, there were 222,119 user-level mentions of CED products in total and the mentions reached an estimated total of 351,707,901 users across platforms. The most popular product accounted for 64.8% of the total user-level mentions. Canadian social media company-owned accounts were found for 27 CED brands. Two CED brands posted the most frequently on Twitter and accounted for the greatest reach, together making up 73.9% of the total company-level posts and reaching 62.5% of the total users in 2020. On Instagram/Facebook, the most popular brand accounted for 23.5% of the company-level posts and 81.3% of the reach between July and September 2021. The most popular marketing techniques used by Canadian CED brands were the use of viral marketing strategies (82.3% of Twitter posts and 92.5% of Instagram/Facebook posts) and the presence of teen themes (73.2% of Twitter posts and 39.4% of Instagram/Facebook posts).ConclusionCED companies are extensively promoting their products across social media platforms using viral marketing strategies and themes that may appeal to adolescents. These findings may inform CED regulatory decision-making. Continued monitoring is warranted.

Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts.

Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France. This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis. This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets. A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies. Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

Differentiation and unity: A Cross-platform Comparison Analysis of Online Posts’ Semantics of the Russian–Ukrainian War Based on Weibo and Twitter

The current Russian–Ukrainian War sparked a new wave of misinformation across social media. However, there is a lack of cross-platform research approaches around war events. This article followed the path of comparative media analysis. We used a three-step method from Triangulation theory and gathered 309,260 relevant posts from both the Twitter and Weibo platforms. We found that (1) Weibo posts are synchronized with Chinese mainstream media, and there is an “Amplification” phenomenon; Twitter posts are delicate and provocative from the perspective of individual encounters; (2) the topics of Weibo are “wide and scattered,” together to form a panoramic broadcast of the conflict. While topics of Twitter formed a condemnation around the invasion war; (3) the positive and negative emotion volume has gone through three stages: “Confrontation,” “Polarization,” and “Extension” with the development of the war. Finally, though the two social media fields present different characteristics, the call for humanitarianism and peace constitutes the unity of public opinion.

Social Media is not a Health Proxy: Differences Between Social Media and Electronic Health Record Reports of Post-COVID Symptoms

The COVID-19 pandemic transformed many aspects of health and daily life. A subset of people who were infected with the virus have ongoing chronic health issues that range in type of symptom and severity. In this study, we conducted a qualitative assessment of self-reported post-COVID symptoms from patients' electronic health records (EHR, n=564) and a randomized collection of Reddit and Twitter posts (n=500 for each). We show the inconsistencies in what types of symptoms are shared between platforms in addition to assessing the severity of the symptoms and how social media characterizations of post-COVID do not tell a complete story of this phenomenon. This research contributes to CSCW health literature by connecting digital traces of post-COVID with EHR data, critiquing the use of social media as a health proxy and points to its potential to add context to the analysis of traditional health data extracted from the EHR.

Dynamics of Negative Discourse toward COVID-19 Vaccines: A Topic Modeling Study and an annotated dataset of Twitter Posts.

Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. A significant portion of these discussions takes place openly on social media platforms. This allows us to closely monitor the opinions of different groups and their changes over time. This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have negative stances toward vaccines. We look into the evolution of the percentage of negative tweets over time. We also examine the different topics discussed in these tweets in order to understand the concerns and discussion points of those holding a negative stance toward the vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected covering the period from March 1, 2020, to July 31, 2021. We used the Scikit-learn Python library to apply a support vector machine (SVM) classifier to identify the tweets with a negative stance toward COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier, out of which a subset of 2,484 tweets were manually annotated by us and made publicly available along with this paper. We used the BERTtopic model to extract and investigate the topics discussed within the negative tweets and how they changed over time. We show that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine roll-outs. We identify 37 topics of discussion and present their respective importance over time. We show that popular topics consist of conspiratorial discussions such as 5G towers and microchips, but also contain legitimate concerns around vaccination safety and side effects as well as concerns about policies. The most prevalent topic among vaccine-hesitant tweets is related to the use of mRNA and fears about speculated negative effects on our DNA. Hesitancy toward vaccines existed prior to COVID-19. However, given the dimension and circumstances surrounding the COVID-19 pandemic, some new areas of hesitancy and negativity toward the COVID-19 vaccines have arisen, for example, whether there has been enough time for them to be properly tested. There is also an unprecedented amount of conspiracy theories associated with them. Our study shows that even unpopular opinions or conspiracy theories can become widespread when paired with a widely popular discussion topic such as COVID-19 vaccines. Understanding the concerns and the discussed topics and how they change over time is essential for policymakers and public health authorities to provide better in-time information and policies, to facilitate vaccination of the population in future similar crises.

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Social media platforms, such as Twitter, have been paramount in the COVID-19 context due to their ability to collect public concerns about the COVID-19 vaccination campaign, which has been underway to end the COVID-19 pandemic. This worldwide campaign has heavily relied on the actual willingness of individuals to get vaccinated independently of the language they speak or the country they reside. This study analyzes Twitter posts about Pfizer/BioNTech, Moderna, AstraZeneca/Vaxzevria, and Johnson & Johnson vaccines by considering the most spoken western languages. Tweets were sampled between April 15 and September 15, 2022, after the injections of at least three doses, collecting 9,513,063 posts that contained vaccine-related keywords. To determine the success of vaccination, temporal and sentiment analysis have been conducted, reporting opinion changes over time and their corresponding events whenever possible concerning each vaccine. Furthermore, we have extracted the main topics over languages providing potential bias due to the language-specific dictionary, such as Moderna in Spanish, and grouped them per country. Once performed the pre-processed procedure we worked with 8,343,490 tweets. Our findings show that Pfizer has been the most debated vaccine worldwide, and the main concerns have been the side effects on pregnant women and children and heart diseases.

The Perception and Usage of Fake Braces: Twitter Content Analysis

Introduction: Financial concerns deter many people from seeking orthodontic care. Fake braces replicate the real orthodontic braces but are not functional and are priced at a lower cost. These products are available through illegal and unregistered merchants, who use social media for advertising their products. This study aimed to analyze Arabic Twitter posts related to fake braces. Methods: Tweets were collected from Twitter API v2 using Python. The search was limited to the Arabic language, using keywords such as “fake braces” and “fashion braces.” Tweets were collected in the period from January 1, 2020, until July 31, 2021. Both sentiment and thematic analyses were performed. Results: 788 tweets met the inclusion criteria, 660 of which were advertisements. The rest of the tweets (n = 128) were categorized into four main themes: rejecting the idea of fake braces (49.2%), having the intention to buy fake braces (32.0%), using/used fake braces (14.1%), and accepting the idea of fake braces (4.7%). Sentiment analysis showed that more than half of the tweets (55.5%) were negative and mainly involved sarcastic language (65.8%). Conclusion: Although many of the tweets were against the use of fake braces, a sizable number of advertisement tweets promoted fake braces. Efforts should be made to educate the public about the risks of using fake braces and to emphasize the importance of seeking a licensed orthodontist for treatment. In addition, health authorities must establish laws and policies and enhance surveillance to control social media content advertising these harmful products.

Social Media Sentiment about COVID-19 Vaccination Predicts Vaccine Acceptance among Peruvian Social Media Users the Next Day.

Drawing upon theories of risk and decision making, we present a theoretical framework for how the emotional attributes of social media content influence risk behaviors. We apply our framework to understanding how COVID-19 vaccination Twitter posts influence acceptance of the vaccine in Peru, the country with the highest relative number of COVID-19 excess deaths. By employing computational methods, topic modeling, and vector autoregressive time series analysis, we show that the prominence of expressed emotions about COVID-19 vaccination in social media content is associated with the daily percentage of Peruvian social media survey respondents who are vaccine-accepting over 231 days. Our findings show that net (positive) sentiment and trust emotions expressed in tweets about COVID-19 are positively associated with vaccine acceptance among survey respondents one day after the post occurs. This study demonstrates that the emotional attributes of social media content, besides veracity or informational attributes, may influence vaccine acceptance for better or worse based on its valence.

Physical- and Social-Based Rain Gauges—A Case Study on Urban Flood Detection

Floods are among the most frequent and costly rainfall-triggered disasters. In this context, geospatial content generated by non-professionals using geolocated systems offers the possibility of monitoring environmental events. This study shows a statistical correlation between in situsensors, radar, Twitter posts, and flooding events. Furthermore, we observed in this study that flooding-related keywords are statistically more significant on flooding days than on non-flooding days and reinforce that Twitter can be employed as a complementary data source for flood management systems.

Sentiment Analysis On Twitter Posts About The Russia and Ukraine War With Long Short-Term Memory

Sentiment analysis is one method for evaluating public opinion from the received text. In this study, we evaluate the performance of the LSTM model with Sastrawi in sentiment analysis in Indonesian using a Twitter dataset totaling 2537 data collected regarding the Russo-Ukrainian war. The purpose of this study is to determine the reliability of the LSTM model with Sastrawi in sentiment analysis in Indonesian and to evaluate the performance of the model with the collected Twitter dataset regarding the Russian-Ukrainian war. The method used in this study is data pre-processing, training and validation of the LSTM model with Literature, and model evaluation using the metrics of accuracy, precision, recall, and F1 score. In the dataset collected in this study, positive, neutral and negative sentiments were 54.7%, 35% and 10.2%. The results obtained from this study indicate that the LSTM model with Literature can provide good results in sentiment analysis with a prediction accuracy of 82%. The implication of the results of this study is that the LSTM model with Sastrawi can be used for sentiment analysis on Twitter and further research needs to be carried out with a wider and more diverse dataset, especially to produce even better accuracy.

TÜRKİYE’DE DİJİTAL AKTİVİZM: KADIN HAREKETİNİN BİR SEMBOLÜ OLARAK İSTANBUL SÖZLEŞMESİ

ABSTRACT
 Digital platforms that allow individuals to interact and offer a public space where people can share the content they produce and seek their rights play a crucial role in organizing activist protests, promoting new representations, and presenting adversary political voices. New activism, called 'digital activism,' is the activities by individuals or organizations in the digital environment to create the social change they desire. Social movements that started on these platforms with the claim of a better world while expressing social discomforts that are unsuitable for the flow of life may also offer new solutions.
 This study reveals the social and political dimensions of how digital activism is shaped in Turkey through the Istanbul Convention, which appears to be the symbol where women, and non-governmental organizations, have struggled for years. The social struggle for both the existence and the implementation of the protection mechanisms of the Istanbul convention, was terminated by the Presidential decree, was analyzed with the content analysis method. Twitter posts under the hashtags "#İstanbulSözleşmesiYaşatır," (IstanbulConventionGivesLife), "#İstanbulSözleşmesindenVazgeçmiyoruz," (WeWon'tAbandonTheIstanbulConvention), "#İstanbulSözleşmesi" (IstanbulConvention) and explored categories related to the reflections of digital activism in Turkey was interpreted. According to the results, while the categories such as informing the public, not yielding, acting together for the benefit of society, and not giving up in the online struggle come to the fore, it is seen that digital is actively used in transferring activism offline. Another important finding of the research is that the main opposition party, individual activists, bar associations, and lawyers, especially women's non-governmental organizations, come to the fore as the most active groups and play an active role outside of digital platforms by directing the society.
 Keywords: Digital activism, Istanbul convention, Right-wing populism, Twitter hashtag, Women’s movements

Do politicians connect with young people? Analysis of Twitter use by candidates in the 2019 European Parliament election campaign

The use of social media, especially Twitter, has become part of the political communication strategies of parties and candidates. Reaching young voters and reducing their historical abstention has become a challenge, even more for candidates standing for the European Parliament, elections that have traditionally witnessed a much smaller turnout. To find out if the main six candidates in the 2019 European Parliament election campaign (10–26 May), called to occupy the presidency of the European Commission, connected with younger voters, this research applies the multiple-case study based on the analysis of their Twitter posts. The results show that candidates (Spitzenkandidaten) preferred to talk about tops such as territory, vote appeal, and their parties’ alliances above the topics that most interest young people: climate change and environment, education, poverty and inequalities, unemployment, human rights and democracy and health. Better political social media communication is needed to make candidates’ communication strategies more consistent and to promote voters’ participation, especially among youth.

A Cross-Platform Exploratory Study of International and Domestic Scholar Post Engagement on Social Media

Social media use varies globally across cultures, even within extension services. Recognition of the contributions of international scholars at the universities where they serve also varies. Social media use in international scholars’ countries of origin may differ from that of their institution’s country, affecting social media engagement with a university’s posts featuring international scholars. Using the conceptual framework of audience segmentation, this study explored the differences in social media audience engagement between research themes and international and domestic scholars on Instagram and Twitter. Using a causal-comparative design, this study created Instagram and Twitter posts highlighting peer-reviewed research conducted by both international and domestic scholars at a United States university in a variety of research themes. Separate Instagram and Twitter posts were created for each scholar. Scholars were categorized by research theme and as either international or domestic. Engagement metrics were analyzed descriptively to explore the differences between research themes and international and domestic scholars across Instagram and Twitter audiences. Advances in food science and safety received the highest engagement rate on Instagram, but the lowest engagement rate on Twitter. International scholars received more engagement than domestic scholars in all categories on Instagram. Domestic scholars had higher mean engagements and engagement rates by impressions than international scholars on Twitter, but international scholars received more mean impressions. The results revealed a scholar’s research theme and status as either international or domestic may influence the level of social media engagement they receive on a specific social media platform. Recommendations for research are discussed.

You Have to Send the Right Message: Examining the Influence of Protective Action Guidance on Message Perception Outcomes across Prior Hazard Warning Experience to Three Hazards

Abstract A long-term goal for warning-message designers is to determine the most effective type of message that can instruct individuals to act quickly and prevent loss of life and/or injury when faced with an imminent threat. One likely way to increase an individual’s behavioral intent to act when they are faced with risk information is to provide protective action information or guidance. This study investigated participant perceptions (understanding, believing, personalizing, deciding, milling, self-efficacy, and response efficacy) in response to the National Weather Service’s experimental product Twitter messages for three hazard types (tornado, snow squall, and dust storm), with each message varying by inclusion and presentation of protective action information placed in the tweet text and the visual graphic. We also examine the role of prior hazard warning experience on message perception outcomes. To examine the effects, the experiment used a between-subjects design in which participants were randomly assigned to one hazard type and received one of four warning messages. Participants then took a post-test measuring message perceptions, efficacy levels, prior hazard warning experience, and demographics. The results showed that, for each hazard and prior hazard experience level, messages with protective action guidance in both the text and graphic increase their understanding, belief, ability to decide, self-efficacy, and response efficacy. These results reinforce the idea that well-designed messages that include protective action guidance work well regardless of hazard type or hazard warning experience. Significance Statement Preventing injury and/or loss of life during a hazardous event is a prime concern for disaster communicators. The study provides insights to practitioners on how to effectively communicate protective actions to audiences with varying familiarity with the hazard through Twitter posts. We experimented with tweet message design and content for three hazards: tornado, snow squall, and dust storm, to find that posts that include protective action guidance in both the text and image increase participant perceptions that they could perform the suggested protective actions, regardless of hazard type or hazard warning experience. Given our findings, practitioners should consider including protective action guidance in message text and graphic to warn members of the public with varied prior warning experience.

#bnefloods: An analysis of the Queensland Government media conferences during the 2022 Brisbane floods

Residents look to state leaders during high-risk events for advice and guidance. Previous events have reinforced the vital role that governments play in delivering emergency messages. Creating meaningful messages during emergencies is vital to inform affected populations and to encourage them to take appropriate actions. In February 2022, South East Queensland experienced a significant flooding event that resulted in the loss of life and property. This study identified that conflicting information was released due to the uncertainty of the weather event. Many residents used Twitter to post about what was being experienced, creating a real-time display of what was happening. This study analysed the public-facing government media conferences held over 4 days against Situational Crisis Communication Theory1 and compared the response to Twitter posts during the key days of the February flood. The study found an overwhelming use of the theory’s denial and diminishment approaches used by some communicators combined with the rebuilding approach used by others. This had great potential to cause conflicting and confusing messages. This research is important because language encourages people to believe that floods can be prevented. Conflicting messaging can cause significant harm by not providing clear direction regarding evacuation and other safety measures.

Analysis of Recognition of Twitter Posts using a Hybrid Machine Learning Approach

Sentiment analysis of tweets has become a crucial task in understanding public opinion, sentiment trends, and brand perception. With the exponential growth of social media data, efficient and accurate sentiment analysis methods are essential. This research paper presents a comprehensive study on the design, simulation, and assessment of sentiment analysis of tweets using an improved machine learning methodology. The proposed methodology combines the strengths of natural language processing techniques and advanced machine learning algorithms to achieve superior performance in sentiment classificationIn the introduction, the paper outlines the significance of sentiment analysis and its applications in various domains. It discusses the challenges faced by traditional sentiment analysis approaches and highlights the need for more robust and accurate methodologies. The paper then delves into related works, exploring the existing state-of-the-art techniques and identifying gaps that the proposed methodology aims to address.The proposed methodology section details the steps involved in the sentiment analysis pipeline. It begins with data preprocessing, including tokenization, stop-word removal, and stemming. Feature extraction methods, such as TF-IDF and word embeddings, are explored and compared. Next, the paper presents an improved machine learning algorithm that combines ensemble learning and deep learning techniques. The model's architecture and training process are elaborated, along with the parameter tuning strategies to optimize performance.The results indicate that the proposed methodology outperforms traditional sentiment analysis approaches, achieving higher accuracy and robustness in sentiment classification. The paper highlights the model's strengths in handling sarcasm, irony, and context-specific language, which are common challenges in sentiment analysis. Furthermore, the efficiency of the proposed methodology is demonstrated through its ability to handle large-scale datasets in real-time. In conclusion, this research paper emphasizes the importance of sentiment analysis in understanding public opinion and its role in decision-making processes for businesses and governments. The proposed methodology exhibits promising results, making it a viable solution for sentiment analysis of tweets and other social media data. The study concludes by suggesting future research directions to further enhance sentiment analysis techniques and address emerging challenges in the field.

Keeping you posted: analysis of fertility-related social media posts after introduction of the COVID-19 vaccine

Purpose Our objective was to analyse information and sentiments posted regarding the COVID-19 vaccine on fertility-related social media. Materials and Methods The first fifty accounts on Instagram and Twitter were identified with the terms: fertility doctor, fertility, OBGYN, infertility, TTC, IVF. Accounts were categorised as physician (PH), individual (ID), or fertility center/organisation (FCO). The vaccine was approved on 12/11/2020 and Instagram and Twitter posts dated 12/1/2020 − 2/28/2021 were reviewed. Posts were analysed for sentiment, mention of research studies (RS), national guidelines (NG), personal experience (PE), side effects (SE), reproductive related (RR) content and activity, including likes and comments. Results A total of 276 accounts were included. Sentiments towards the vaccine were largely positive (PH 90.3%, ID 71.4%, FCO 70%), or neutral (PH 9.7%, ID 28.6%, FCO 30%). Instagram accounts showed an increase in activity on vaccine posts compared to baseline by likes (PH 4.86% v 3.76%*, ID 7.5% v 6.37%*, FCO 2.49% v 0.52%*) and comments (PH 0.35% v 0.28%, ID 0.90% v 0.69%,* FCO 0.10% v 0.02%*). Conclusion Most posts expressed positive sentiments towards the vaccine. Evaluating the sentiment of the COVID-19 vaccine as it relates to fertility on social media represents an opportunity for understanding both the patient’s and health care professional’s opinion on the subject. Given the potential devastating effects of misinformation on public health parameters, like vaccination, social media offers one avenue for healthcare professionals to engage online and work to make their presences more effective and influential. SHORT CONDENSATION This article analyses content and sentiments posted regarding the COVID-19 vaccine on fertility-related social media in order to offer a deeper understanding of available information and beliefs.

Social media branding in the food industry: comparing B2B and B2C companies’ use of sustainability messaging on Twitter

PurposeSupply chains are under increased scrutiny as consumers have become aware of the dark side of downstream production practices. Many articles and exposés have led consumers to be more conscious of purchasing products from companies who source materials in a socially responsible and ethical manner. As a result, business-to-business (B2B) and business-to-consumers (B2C) companies are under increased pressure to source raw materials in a transparent and ethical way. Because of the associated costs, companies then look to benefit from increased brand equity by promoting to consumers how ethical their products are. The purpose of this study is to look at the case of the food industry to analyze sustainability messaging on Twitter in both B2B and B2C companies to determine which of the dimensions of sustainability (people, profit, planet) are being emphasized.Design/methodology/approachIn this study, two published dictionaries were combined to capture the three dimensions of “sustainability,” and these scales were then used to analyze Twitter posts. This study created a unique software package to classify, mine, collect and analyze Twitter data. This study used these tools to analyze 246,386 Twitter posts within a sample of 39 leading B2C and B2B food companies over a 10-year period (2012–2021) to explore brand messaging and engagement rate.FindingsThis research revealed several interesting results. These include how B2B companies have emphasized the employee (people) dimensions of sustainability, while B2C companies have had a more balanced approach that overall has prioritized the economic dimension (profit) of sustainability. The data on audience engagement revealed a mismatch between the types of sustainability messaging B2B companies and B2C companies in the food industry are posting and what engages audiences.Originality/valueThis study fills several gaps, including analyzing how B2B and B2C companies use sustainability language in their social media brand management, as well as looking at which dimensions of sustainability they emphasize and which ones engage audiences the most. This research is also novel in combining multiple existing scales under one project to analyze the triple bottom line in the analysis of qualitative texts.

The Ripple Effect of Firm-Generated Content on New Movie Releases

Marketers frequently create social media content (i.e., firm-generated content; FGC) to ignite interest in new movies. Thus, there is a clear need to understand the magnitude and heterogeneity of the effect of FGC on movie demand and associated user-generated content (UGC). The authors empirically examine the complex interactions among FGC, UGC, and sales using social media (tweet) data that are normally available to firms. They investigate two potential mechanisms by which FGC may drive box office revenues: (1) a direct mechanism, such that users who see FGC directly drive revenue, and (2) an indirect “ripple effect,” by which FGC increases movie-related UGC, which then drives consumption. By analyzing 145,502 firm-generated and 5.9 million user-generated Twitter posts associated with 159 movies, the authors find a positive and significant effect of FGC on movie sales, which UGC fully mediates, which supports the indirect ripple effect reasoning. Impressions of FGC by followers of firm accounts, as opposed to nonfollowers of firm accounts, mainly drive the effect of FGC on UGC. In addition, FGC by movie accounts is more effective than that by actors and studios. Firms’ regular posts with a movie-specific hashtag are more effective than replies, retweets, and posts without the hashtag. The finding of the ripple effect suggests that movie executives should focus on creating FGC that sparks conversations among followers when new movies are released.