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1.
This paper examines how alternative food networks (AFNs) cultivate engagement on a social media platform. Using the method proposed in Kar and Dwivedi (2020) and Berente et al. (2019), we contribute to theory through combining exploratory text analysis with model testing. Using the theoretical lens of relationship cultivation and social media engagement, we collected 55,358 original Weibo posts by 90 farms and other AFN participants in China and used Latent Dirichlet Allocation (LDA) modeling for topic analysis. We then used the literature to map the topics with constructs and developed a theoretical model. To validate the theoretical model, a panel dataset was constructed on Weibo account and year level, with Chinese city-level yearly economic data included as control variables. A fixed effects panel data regression analysis was performed. The empirical results revealed that posts centered on openness/disclosure, sharing of tasks, and knowledge sharing result in positive levels of social media engagement. Posting about irrelevant information and advertising that uses repetitive wording in multiple posts had negative effects on engagement. Our findings suggest that cultivating engagement requires different relationship strategies, and social media platforms should be leveraged according to the context and the purpose of the social cause. Our research is also among the early studies that use both big data analysis of large quantities of textual data and model validation for theoretical insights. 相似文献
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《Information processing & management》2022,59(4):102959
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weibo, the largest Twitter-like social media platform in China, from 2009 to 2019. The newly collected data comprise 44,728 posts in the nine domains, published by 40,215 users, and reposted over 3.4 million times. Based on the distributions and spreads of the multi-domain dataset, we observe that false news in domains that are close to daily life like health and medicine generated more posts but diffused less effectively than those in other domains like politics, and that political false news had the most effective capacity for diffusion. The widely diffused false news posts on Weibo were associated strongly with certain types of users — by gender, age, etc. Further, these posts provoked strong emotions in the reposts and diffused further with the active engagement of false-news starters. Our findings have the potential to help design false news detection systems in suspicious news discovery, veracity prediction, and display and explanation. The comparison of the findings on Weibo with those of existing work demonstrates nuanced patterns, suggesting the need for more research on data from diverse platforms, countries, or languages to tackle the global issue of false news. The code and new anonymized dataset are available at https://github.com/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News. 相似文献
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The content generation strategy of a sports franchise determines whether the user engagement increases or decreases on social media platforms. Thus, the role of Chief Operating Officer (COO) is profound who generally decides and governs social media policies of the franchises. We show that the cultural differences between local-COO vis-à-vis foreign-COO-governed sports franchises reflect in their content generation strategy and are also associated with user engagement. We use Hofstede's cultural dimensions theory and extract relevant features from the tweets. Overall, the results show that user engagement is more when the content generation strategy is in alignment with fans’ national culture. The first contribution of our work is towards showing the incremental impact of power distance, individualism and collectivism on user engagement. The second contribution of our work is towards feature construction, feature selection and building authorship attribution classifiers to understand the content generation strategy. Prior literature shows that national culture impacts writing of online reviews. We investigate the role of national culture in social media content generation and user engagement and extend the literature. Our study is useful for organizations to understand the role of national culture in content generation and how it is related to user engagement. 相似文献
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基于社会化商务的商务模式创新策略：以社会化商务渠道选择策略为例 《科学学研究》2015,33(8):1271-1280
需求低增速、竞争加剧,是经济增长放缓时企业面临的常见挑战。商务模式创新是关键应对措施之一。本文以营销领域的社会化商务渠道选择策略为例探讨了日益重要、基于社会化商务的商务模式创新策略。运用基于主体的计算实验方法和广义虚拟经济使用价值、虚拟价值视角,构建了涉及有限的评价理性、评论影响、渠道价值展现程度等关键因素的社会化商务应用模型。本文提出三种有利于增加销量的社会化商务渠道选择策略：选择倾向高估或能比较理性评价产品价值的客户（率先尝新者）比例较高的渠道;选择有影响力客户比例较高的渠道;可在运用基于主体的计算实验等就渠道使用价值、虚拟价值展现程度对销量的影响进行评估后,再选择能更好地展现产品价值的渠道。并主张如下基于社会化商务的商务模式创新策略：在基于主体的计算实验等复杂性科学研究方法支持下,充分结合评论、分享等互联网社会化活动特点和社会化商务渠道特性来探索有利于提高参与者价值的商务模式。 相似文献
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The increased availability of social media big data has created a unique challenge for marketing decision-makers; turning this data into useful information. One of the significant areas of opportunity in digital marketing is influencer marketing, but identifying these influencers from big data sets is a continual challenge. This research illustrates how one type of influencer, the market maven, can be identified using big data. Using a mixed-method combination of both self-report survey data and publicly accessible big data, we gathered 556,150 tweets from 370 active Twitter users. We then proposed and tested a range of social-media-based metrics to identify market mavens. Findings show that market mavens (when compared to non-mavens) have more followers, post more often, have less readable posts, use more uppercase letters, use less distinct words, and use hashtags more often. These metrics are openly available from public Twitter accounts and could integrate into a broad-scale decision support system for marketing and information systems managers. These findings have the potential to improve influencer identification effectiveness and efficiency, and thus improve influencer marketing. 相似文献
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《Information processing & management》2022,59(2):102807
Social sensing has become an emerging and pervasive sensing paradigm to collect timely observations of the physical world from human sensors. In this paper, we study the problem of geolocating abnormal traffic events using social sensing. Our goal is to infer the location (i.e., geographical coordinates) of the abnormal traffic events by exploring the location entities from the content of social media posts. Two critical challenges exist in solving our problem: (i) how to accurately identify the location entities related to the abnormal traffic event from the content of social media posts? (ii) How to accurately estimate the geographic coordinates of the abnormal traffic event from the set of identified location entities? To address the above challenges, we develop a Social sensing based Abnormal Traffic Geolocalization (SAT-Geo) framework to accurately estimate the geographic coordinates of abnormal traffic events by exploring the syntax-based patterns in the content of social media posts and the geographic information associated with the location entities from the social media posts. We evaluate the SAT-Geo framework on three real-world Twitter datasets collected from New York City, Los Angeles, and London. Evaluation results demonstrate that SAT-Geo significantly outperforms state-of-the-art baselines by effectively identifying location entities related to the abnormal traffic events and accurately estimating the geographic coordinates of the events. 相似文献
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《Information processing & management》2019,56(3):687-703
Popular events are well reflected on social media, where people share their feelings and discuss their experiences. In this paper, we investigate the novel problem of exploiting the content of non-geotagged posts on social media to infer the users’ attendance of large events in three temporal periods: before, during and after an event. We detail the features used to train event attendance classifiers and report on experiments conducted on data from two large music festivals in the UK, namely the VFestival and Creamfields events. Our classifiers attain very high accuracy with the highest result observed for the Creamfields festival ( ∼ 91% accuracy at classifying users that will participate in the event). We study the most informative features for the tasks addressed and the generalization of the learned models across different events. Finally, we discuss an illustrative application of the methodology in the field of transportation. 相似文献
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The rising popularity of social media posts, most notably Twitter posts, as a data source for social science research poses significant problems with regard to access to representative, high-quality data for analysis. Cheap, publicly available data such as that obtained from Twitter's public application programming interfaces is often of low quality, while high-quality data is expensive both financially and computationally. Moreover, data is often available only in real-time, making post-hoc analysis difficult or impossible. We propose and test a methodology for inexpensively creating an archive of Twitter data through population sampling, yielding a database that is highly representative of the targeted user population (in this test case, the entire population of Japanese-language Twitter users). Comparing the tweet volume, keywords, and topics found in our sample data set with the ground truth of Twitter's full data feed confirmed a very high degree of representativeness in the sample. We conclude that this approach yields a data set that is suitable for a wide range of post-hoc analyses, while remaining cost effective and accessible to a wide range of researchers. 相似文献
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《International Journal of Information Management》2017,37(3):229-240
Company social networks have become an important means for the socialized marketing of a company, forming a new challenge to companies on how to attract customers. Based on such theories as customer engagement, value co-creation, and relationship marketing, this paper presents a model of the influence of customer engagement on stickiness. Data collected from 260 valid questionnaires from Sina’s enterprise microblog users were analyzed by structural equation modeling. Empirical results show that customer engagement has a direct and positive influence on customer stickiness as well as an indirect influence through customer value creation. This study enriches previous researches on existing theories of customer engagement, value co-creation, and stickiness, and gives practical guidance for companies to encourage customer engagement and enhance the stickiness of company social networks. 相似文献
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This study furthers investigation into exactly how Social CRM (S-CRM) is different from traditional CRM, and models the interrelationships between its capabilities. It is underpinned in dynamic capabilities theory, to explain how social media, as a resource all organizations use, can lead to differing performance outcomes. It is underpinned in seminal research into traditional CRM, but which does not cater for the disruptive nature of social media. We outline how S-CRM is a second-order dynamic capability consistng of a set of first-order integrative dynamic capabiliies that, when properly interrelated, lead to performance outcomes. We particularly model the role of S-CRM front- and back-office technology capabilities, customer engagement initiatives, and social information processes in driving customer relationship performance. Findings show that S-CRM is different from traditional CRM in a range of ways in the front- and back-offices, and provide a framework for researcher and managers in information systems and marketing to operate at strategic and tactical levels within S-CRM, while being congisant of both. 相似文献
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《Information processing & management》2022,59(4):102961
Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for complex mental state reasoning. Besides, they are not designed to explicitly capture class-specific features. To resolve the above issues, we propose a mental state Knowledge–aware and Contrastive Network (KC-Net). In detail, we first extract mental state knowledge from a commonsense knowledge base COMET, and infuse the knowledge using Gated Recurrent Units (GRUs) to explicitly model the mental states of the speaker. Then we propose a knowledge–aware mentalisation module based on dot-product attention to accordingly attend to the most relevant knowledge aspects. A supervised contrastive learning module is also utilised to fully leverage label information for capturing class-specific features. We test the proposed methods on a depression detection dataset Depression_Mixed with 3165 Reddit and blog posts, a stress detection dataset Dreaddit with 3553 Reddit posts, and a stress factors recognition dataset SAD with 6850 SMS-like messages. The experimental results show that our method achieves new state-of-the-art results on all datasets: 95.4% of F1 scores on Depression_Mixed, 83.5% on Dreaddit and 77.8% on SAD, with 2.07% average improvement. Factor-specific analysis and ablation study prove the effectiveness of all proposed modules, while UMAP analysis and case study visualise their mechanisms. We believe our work facilitates detection and analysis of depression and stress on social media data, and shows potential for applications on other mental health conditions. 相似文献
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作为MSI两次提及的优先研究领域,顾客契合成为当前国际营销科学领域的热点问题之一。研究基于知识图谱理论,利用Citespace软件对Web of Science核心合集上1076篇顾客契合相关文献进行文献计量分析,绘制顾客契合研究文献的共被引知识图谱和共词聚类知识图谱,以探索顾客契合的研究热点和研究趋势。研究发现：顾客契合当前研究热点包括问卷测量开发、顾客契合实证研究和顾客契合在价值创造中的作用;顾客契合的研究趋势包括大数据环境下的顾客契合测量、顾客契合价值的识别与挖掘、在线互动环境中的“游戏化”元素设计研究三个方面。 相似文献
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《Information processing & management》2022,59(5):103009
The rapid development of online social media makes Abusive Language Detection (ALD) a hot topic in the field of affective computing. However, most methods for ALD in social networks do not take into account the interactive relationships among user posts, which simply regard ALD as a task of text context representation learning. To solve this problem, we propose a pipeline approach that considers both the context of a post and the characteristics of interaction network in which it is posted. Specifically, our method is divided into pre-training and downstream tasks. First, to capture fine contextual features of the posts, we use Bidirectional Encoder Representation from Transformers (BERT) as Encoder to generate sentence representations. Later, we build a Relation-Special Network according to the semantic similarity between posts as well as the interaction network structural information. On this basis, we design a Relation-Special Graph Neural Network (RSGNN) to spread effective information in the interaction network and learn the classification of texts. The experiment proves that our method can effectively improve the detection effect of abusive posts over three public datasets. The results demonstrate that injecting interaction network structure into the abusive language detection task can significantly improve the detection results. 相似文献
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《Information processing & management》2022,59(1):102762
Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness. 相似文献
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《Information processing & management》2020,57(5):102220
Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns. 相似文献
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《Information processing & management》2020,57(6):102313
Social media is widely used for sharing disaster-related information following natural disasters. Drawing on negativity bias theory, integrated crisis mapping model, and arousal theory, this study characterized the emotional responses of the public and tested the way emotional factors and influential users (with high numbers of followers and activeness) affect the number of reposts. Results indicated that after unpredictable earthquakes, the public showed negative responses, and negativity bias theory manifested especially when the posts came from influential users. During a typhoon or earthquake, the number of reposts grew as the number of anger-related words in posts increased. Anxiety- and typhoon-related posts from users with high numbers of followers negatively affected the number of reposts, whereas sadness-related posts had contrasting effects. These findings can help emergency managers formulate proper emotional response strategies after various natural calamities and help researchers test the abovementioned theories or models using real-word data from social media. 相似文献
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The use of the internet and social media have changed consumer behavior and the ways in which companies conduct their business. Social and digital marketing offers significant opportunities to organizations through lower costs, improved brand awareness and increased sales. However, significant challenges exist from negative electronic word-of-mouth as well as intrusive and irritating online brand presence. This article brings together the collective insight from several leading experts on issues relating to digital and social media marketing. The experts’ perspectives offer a detailed narrative on key aspects of this important topic as well as perspectives on more specific issues including artificial intelligence, augmented reality marketing, digital content management, mobile marketing and advertising, B2B marketing, electronic word of mouth and ethical issues therein. This research offers a significant and timely contribution to both researchers and practitioners in the form of challenges and opportunities where we highlight the limitations within the current research, outline the research gaps and develop the questions and propositions that can help advance knowledge within the domain of digital and social marketing. 相似文献
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《Information processing & management》2020,57(6):102348
Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (Naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of analysing social media feeds particularly in interpreting slangs/acronyms/abbreviations that have contextually rooted meanings. 相似文献