Beneath the hashtag: multifaceted insights into sustainable consumption and production from historical Twitter data

Sustainability Science Practice and Policy, Год журнала: 2025, Номер 21(1)

Опубликована: Янв. 4, 2025

This research contributes to the ongoing dialogue on sustainable product categorization and representation in social media. Companies consumers are increasingly favoring eco-friendly, green, or products due their environmentally friendly lifecycle. study examines a dataset of more than 30,000 tweets labeled with hashtags such as #sustainableproducts, #ecoproducts, #ecofriendlyproducts, #greenproducts. The covers ten years includes Twitter data from around world. employs content sentiment analysis reveal that conversations green often emphasize environmental impact responsible consumption, reflecting consumer preferences business strategies. Discussions eco-friendly eco-products center materials waste reduction, highlighting choices need for eco-conscious design. general expressed these is primarily positive neutral, although negative sentiments frequently associated concerns plastic pollution. not only highlights significance digital platforms influencing public discussions sustainability but also emphasizes informative power media shaping narratives. It suggests comprehensive strategy encompassing transparent balanced communication, active engagement, collaborative efforts among stakeholders promote consumption platforms, thereby contributing achievement climate neutrality. By shedding light complexities nuances online discourse, it provides foundation future scholarly inquiries informs practical production age.

Язык: Английский

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Pride, not prejudice: a cross-cultural big data analysis of consumer reactions to rainbow-washing in advertising

International Journal of Advertising, Год журнала: 2025, Номер unknown, С. 1 - 28

Опубликована: Март 21, 2025

Язык: Английский

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Holistic approach to analysing debates on ecological sustainability over time on X

EPJ Data Science, Год журнала: 2025, Номер 14(1)

Опубликована: Апрель 17, 2025

Abstract Using network theory and data analysis, we study the messages on Twitter (X) about ecological sustainability over period 2007-2022. With a global view of 70,311,541 examined sentiment, keywords hashtags utilised, as well correlations between sentiment both socioeconomic environmental variables. In addition to above, carried out an in-depth analysis interactions (retweets, replies quotes), with special focus community (CNET) (with 4576 supernodes, 9855 links). The shown in text tweets was positive years all analysed locations, although close neutral. Keyword detected terms present posted from various regions, showing thinking world. relationships variables were continent- country-specific, identifying stronger correlation attributes. Regarding CNET, according performed using adjacency laplacian embeddings, Chebyshev, Euclidean, Minkowski, Manhattan distances, pairs unconnected supernodes appeared have more similarity their connection patterns than connected due topological structure CNET which has large number peripheral nodes that are not each other, but higher centrality. agreement Jaccard coefficient, resource allocation index, Adamic Adar preferential attachment score, there is little possibility link formation supernodes. Statistically also exhibited high similarity. A few specific host most users, highest centralities among those analysed. basic maintained its key properties, examined. Strategies promote communication achieve greater participation diversity discussions need be further investigated.

Язык: Английский

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From ugly to attractive: Leveraging anthropomorphism to increase demand for irregular‐appearing produce

Psychology and Marketing, Год журнала: 2024, Номер 41(9), С. 2033 - 2056

Опубликована: Май 26, 2024

Abstract Recognizing that the waste of imperfect produce contributes to global environmental crisis, authors conduct three studies examining whether retailers can use anthropomorphizing marketing techniques make irregular‐appearing more attractive and increase purchase intentions. Based on exemplar model theory, Study 1 shows when place googly eyes pictures produce, consumers judge product according multiple exemplars as they do evaluate humans. The esthetic cues cause them perceive irregular attractive. 2 replicates by using human names anthropomorphic cues, demonstrating anthropomorphism intentions toward produce. 3 further effects hold for from corporate farms not local farms. differences occur because expect conform standardized norms but market When expectations are aligned, is likely diversify disassociated. article concludes with suggestions promotion strategies change attitudes

Язык: Английский

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Forecasting the Spread of Sustainability Movement: Computational Analysis on Social Media Messages Promoting Climate Actions

Journal of Current Issues & Research in Advertising, Год журнала: 2024, Номер 45(3), С. 282 - 300

Опубликована: Июнь 28, 2024

Язык: Английский

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Mega‐influencers and brand dynamics: Shaping attitudes toward leading and challenger brands through electronic word of mouth

Psychology and Marketing, Год журнала: 2024, Номер 41(12), С. 3041 - 3059

Опубликована: Авг. 19, 2024

Abstract The aim of this paper is to explore how mega‐influencers' electronic word mouth (eWOM) messages on social media influence consumers' brand attitudes in duopolistic markets. Through three experimental studies, we observe that when mega‐influencers send positive (vs. negative) eWOM about a leading brand, followers form attitudes, but these effects fail occur influencers back challenger brands. findings are consistent across market rivals (Apple vs. Samsung; UPS FedEx; Nike Adidas), platforms (Facebook, Instagram, and X), four (Marques Brownlee, Gary Vaynerchuk, Kanye West, Kylie Jenner). Findings indicate have more persuasive power recommending or criticizing brands rather than brands, irrespective their follower base. contribute the marketing literature by showing contexts, e‐WOM has varied followers' depending status market.

Язык: Английский

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Price transparency in international retailing on digital platforms

International Marketing Review, Год журнала: 2024, Номер 41(5), С. 1133 - 1160

Опубликована: Авг. 8, 2024

Purpose In the digital era, price transparency—the practice of disclosing cost breakdowns in product manufacturing—has become present on platforms. Although its benefits are well-documented and consumers should theoretically desire costless relevant information for informed decision-making, this paper proposes that may resist overly transparent pricing, particularly when it pertains to premium-priced (vs regular-priced) products from countries with high equity. Design/methodology/approach Our research comprises three experimental studies utilizing both student representative online Prolific samples, covering various different equity levels. Initially, a pilot study identifies an interpersonal should-want conflict induced by transparency purchasing products, leading avoidance. Subsequent further explore phenomenon examining moderating role country mediating unfairness perceptions. Findings Price can backfire due want-should among consumers—the receive disclosure versus inclination not view it. This results increased resistance receiving decreased brand attitudes purchase intentions, especially originating high-equity countries. Heightened perceptions explain these dynamics. Research limitations/implications The primarily relies designs limited sample sizes. To enhance generalizability findings, incorporating large-scale real market data across diverse domains would be beneficial. Originality/value Grounded avoidance theories, uniquely explores adverse effects We extend demonstrating is influenced equity, where perceived value added association given name affects whether experience conflict. investigation deepens our understanding nuanced transparency.

Язык: Английский

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Examining Implications of Search Query Construction on Sentiment Analysis: The Case of Brandwatch

Journal of Current Issues & Research in Advertising, Год журнала: 2024, Номер 45(3), С. 339 - 356

Опубликована: Июль 2, 2024

Consistent with other fields, advertising research is currently facing a replication crisis, evoking methodological concerns regarding the reproducibility of findings. To address this we systematically reviewed seven computational studies that collected and analyzed sentiment tweets. Upon identifying five reported replicable data collection processes, utilized original search queries to recollect tweets using third-party platform, Brandwatch. We also replicated iterative complex Boolean (ICB) operators, which sought enhance resulting dataset precision. Then, juxtaposed effects query design (original vs. ICB) analysis. Together, findings suggest how written can affect measures precision, engagement, sentiment, recall. Tests precision are recommended mitigate variance in retrieval relevance.

Язык: Английский

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Green with Arrogance: Dominant Brands Benefit from Arrogant Appeals

Journal of Current Issues & Research in Advertising, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Май 20, 2024

Consumers increasingly expect brands to contribute positively the environment. This trend has created a race for establish their "green" credentials. Asserting superiority—a strategy we refer as an arrogant appeal—may allow some connect with this consumer preference if perceived credible; however, when brand communication lacks credibility, consumers lose trust and suspect of greenwashing. Applying expectancy violation theory signaling green advertising, find specific message source (level dominance) influences effectiveness appeal. Specifically, via use two experiments, demonstrate that market dominance cue credibility research (a) builds on comparative advertising literature introduce concept appeal literature, (b) identifies new signal—brand dominance—for appeals, (c) extends marketing arrogance design characteristics, (d) discusses ethical ramifications inferring based dominance. A critical finding is more dominant have earned right deploy heuristic.

Язык: Английский

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Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques

Algorithms, Год журнала: 2024, Номер 17(11), С. 486 - 486

Опубликована: Окт. 29, 2024

The advancements in social networking have empowered open expression on micro-blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. This study proposed a classification framework for sentiment data using word count vectorization machine learning techniques reduce the difficulties faced annotated sentiment-labelled tweets. Various classifiers (Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF)) were evaluated based accuracy, precision, recall, F1-score, specificity. outperformed others an Area under Curve (AUC) value of 0.96 average precision (AP) score classification, especially effective minimal Twitter-specific features.

Язык: Английский

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