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1.
The ability of digital influencers to convert their followers into paying customers relies heavily on the followers’ “stickiness”, a topic that has not been adequately investigated in the existing literature. From a psychological perspective, this study develops a theoretical model of followers’ stickiness. Two forms of followers’ psychological responses to digital influencers are jointly considered: wishful identification and parasocial relationships. This study also categorizes and examines the moderating effects exerted by the genres of digital influencers’ revenue models, which represent a vital contextual factor for forming followers’ stickiness. A survey was conducted on Weibo, the Chinese equivalent to Twitter, with a sample of 319 followers of real digital influencers that are using different genres of revenue models. The findings indicate that both wishful identification and parasocial relationships have significant but different impacts on followers’ stickiness in different genres of influencers’ revenue models. This paper enriches our understanding of the phenomenon of followers’ stickiness toward digital influencers and provides practical guidance for digital influencers and social commerce/media platform providers. 相似文献
2.
《Information processing & management》2020,57(2):102031
Social media platforms allow users to express their opinions towards various topics online. Oftentimes, users’ opinions are not static, but might be changed over time due to the influences from their neighbors in social networks or updated based on arguments encountered that undermine their beliefs. In this paper, we propose to use a Recurrent Neural Network (RNN) to model each user’s posting behaviors on Twitter and incorporate their neighbors’ topic-associated context as attention signals using an attention mechanism for user-level stance prediction. Moreover, our proposed model operates in an online setting in that its parameters are continuously updated with the Twitter stream data and can be used to predict user’s topic-dependent stance. Detailed evaluation on two Twitter datasets, related to Brexit and US General Election, justifies the superior performance of our neural opinion dynamics model over both static and dynamic alternatives for user-level stance prediction. 相似文献
3.
《Information processing & management》2023,60(4):103325
Climate change has become one of the most significant crises of our time. Public opinion on climate change is influenced by social media platforms such as Twitter, often divided into believers and deniers. In this paper, we propose a framework to classify a tweet’s stance on climate change (denier/believer). Existing approaches to stance detection and classification of climate change tweets either have paid little attention to the characteristics of deniers’ tweets or often lack an appropriate architecture. However, the relevant literature reveals that the sentimental aspects and time perspective of climate change conversations on Twitter have a major impact on public attitudes and environmental orientation. Therefore, in our study, we focus on exploring the role of temporal orientation and sentiment analysis (auxiliary tasks) in detecting the attitude of tweets on climate change (main task). Our proposed framework STASY integrates word- and sentence-based feature encoders with the intra-task and shared-private attention frameworks to better encode the interactions between task-specific and shared features. We conducted our experiments on our novel curated climate change CLiCS dataset (2465 denier and 7235 believer tweets), two publicly available climate change datasets (ClimateICWSM-2022 and ClimateStance-2022), and two benchmark stance detection datasets (SemEval-2016 and COVID-19-Stance). Experiments show that our proposed approach improves stance detection performance (with an average improvement of 12.14% on our climate change dataset, 15.18% on ClimateICWSM-2022, 12.94% on ClimateStance-2022, 19.38% on SemEval-2016, and 35.01% on COVID-19-Stance in terms of average F1 scores) by benefiting from the auxiliary tasks compared to the baseline methods. 相似文献
4.
《Information processing & management》2022,59(6):103095
Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information. 相似文献
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《Information processing & management》2023,60(4):103366
A large body of research work has proposed verification techniques for rumors spreading in social media that mainly relied on subjective evidence, e.g., propagation networks or user interactions. Alternatively, in this work, we introduce the task of authority finding in social media, in which we aim to find authorities, for given rumors spreading specifically in Twitter, who can help verify them by providing exclusive/convincing evidence that supports or denies those rumors. We release the first test collection for Authority FINding in Arabic Twitter (AuFIN). The collection comprises 150 rumors (expressed in tweets) associated with a total of 1,044 authority accounts and a user collection of 395,231 Twitter accounts (members of 1,192,284 unique Twitter lists). Moreover, we propose a hybrid model that employs pre-trained language models and combines lexical, semantic, and network signals to find authorities. Our experiments show that the textual representation of users is insufficient, and incorporating the Twitter network features improved the recall of authorities by 34%. Moreover, semantic ranking is inferior to the lexical and network-based ranking in terms of precision, but superior in terms of recall. Therefore, combining both the semantic and network-based ranking achieved the best overall performance achieving a precision of 0.413 and 0.213 at depth 1 and 5 respectively. We show that rumor expansion by exploiting Knowledge Bases improves the recall of authorities by up to 15%. Furthermore, we find that SOTA models for topic expert finding perform poorly on finding authorities. Finally, drawing upon our experiments, we discuss failure factors and make recommendations for future research directions in addressing this task. 相似文献
7.
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
8.
In this paper, we propose an investigation of negative reviews and define the profile of negative influencers in Yelp. The methodology adopted to achieve this goal consists of two phases. The first one is theoretical and aims at defining a multi-dimensional social network based model of Yelp, three stereotypes of Yelp users, and a network based model to represent negative reviewers and their relationships. The second phase is experimental and consists in the definition of five hypotheses on negative reviews and reviewers in Yelp and their verification through an extensive data analysis campaign. This was performed on Yelp data represented by means of the models introduced during the first phase. Its most important result is the construction of the profile of negative influencers in Yelp. The main novelties of this paper are: (i) the definition of the two social network based models of Yelp and its users; (ii) the definition of three stereotypes of Yelp users and their characteristics; (iii) the construction of the profile of negative influencers in Yelp. 相似文献
9.
The findings of our experiments showed that social network sites (SNSs) such as Google Plus, Facebook, and Twitter, have the ability to acquire knowledge about their users’ movements not only within SNSs but also beyond SNS boundaries, particularly among websites that embedded SNS widgets such as Google’s Plus One button, Facebook’s Like button, and Twitter’s Tweet button. In this paper, we analysed the privacy implication of such a practice from a moral perspective by applying Helen Nissenbaum’s decision heuristic derived from her contextual integrity framework in order to answer the question of whether or not an online user’s privacy is being violated by this practice. 相似文献
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《Information processing & management》2019,56(3):1146-1165
Detecting events in real-time from the Twitter data stream has gained substantial attention in recent years from researchers around the world. Different event detection approaches have been proposed as a result of these research efforts. One of the major challenges faced in this context is the high computational cost associated with event detection in real-time. We propose, TwitterNews+, an event detection system that incorporates specialized inverted indices and an incremental clustering approach to provide a low computational cost solution to detect both major and minor newsworthy events in real-time from the Twitter data stream. In addition, we conduct an extensive parameter sensitivity analysis to fine-tune the parameters used in TwitterNews+ to achieve the best performance. Finally, we evaluate the effectiveness of our system using a publicly available corpus as a benchmark dataset. The results of the evaluation show a significant improvement in terms of recall and precision over five state-of-the-art baselines we have used. 相似文献
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《Information processing & management》2023,60(2):103206
A news article’s online audience provides useful insights about the article’s identity. However, fake news classifiers using such information risk relying on profiling. In response to the rising demand for ethical AI, we present a profiling-avoiding algorithm that leverages Twitter users during model optimisation while excluding them when an article’s veracity is evaluated. For this, we take inspiration from the social sciences and introduce two objective functions that maximise correlation between the article and its spreaders, and among those spreaders. We applied our profiling-avoiding algorithm to three popular neural classifiers and obtained results on fake news data discussing a variety of news topics. The positive impact on prediction performance demonstrates the soundness of the proposed objective functions to integrate social context in text-based classifiers. Moreover, statistical visualisation and dimension reduction techniques show that the user-inspired classifiers better discriminate between unseen fake and true news in their latent spaces. Our study serves as a stepping stone to resolve the underexplored issue of profiling-dependent decision-making in user-informed fake news detection. 相似文献
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Hani Okoroafor 《知识管理研究与实践》2014,12(1):97-102
Contemporary discourse among knowledge management and franchising scholars points to five key barriers that obstruct franchisors and franchisees from leveraging tacit knowledge as a resource for competitive advantage. Cumberland & Githens (2010, 2012), in their seminal works, identified these as Trust, Maturation, Communication, Competition and Culture. Usually, these barriers have been considered holistically as influencers of tacit knowledge transfer. Thus there has been limited debate on the individual influence of each factor and scant robust testing of these barriers to determine whether they are indeed distinct factors. This paper revisits the ideas that have led to the identification and justification of these barriers, and explores the complex relationships that often exist between franchisors and franchisees, and also between franchisees themselves. Finally, this paper will offer some novel ideas on how these barriers could be mitigated, and tacit knowledge transferred, through better structuring of vertical and horizontal information flows. 相似文献
14.
《Research Policy》2022,51(1):104373
In this paper, we shed new light on the links between firm-level innovation and growth. We introduce data that capture a difficult-to-observe aspect of firms' innovative activity – new product/service launches – at scale. We show that our novel measures complement existing innovation metrics. We build a simple framework covering firm-level innovation, launches and revenue productivity. Then, we show positive linkages between past patenting and launches and between launches and performance for a large panel of small and medium-sized enterprises (SMEs) in the UK. We go on to explore the roles of age, size, industry and product/service quality in these relationships. A subset of SMEs with high-quality launches explains our results. 相似文献
15.
Depression is one of the most common mental health problems worldwide. The diagnosis of depression is usually done by clinicians based on mental status questionnaires and patient's self-reporting. Not only do these methods highly depend on the current mood of the patient, but also people who experience mental illness are often reluctantly seeking help. Social networks have become a popular platform for people to express their feelings and thoughts with friends and family. With the substantial amount of data in social networks, there is an opportunity to try designing novel frameworks to identify those at risk of depression. Moreover, such frameworks can provide clinicians and hospitals with deeper insights about depressive behavioral patterns, thereby improving diagnostic process. In this paper, we propose a big data analytics framework to detect depression for users of social networks. In addition to syntactic and syntax features, it focuses on pragmatic features toward modeling the intention of users. User intention represents the true motivation behind social network behaviors. Moreover, since the behaviors of user's friends in the network are believed to have an influence on the user, the framework also models the influence of friends on the user's mental states. We evaluate the performance of the proposed framework on a massive real dataset obtained from Facebook and show that the framework outperforms existing methods for diagnosing user-level depression in social networks. 相似文献
16.
《Information processing & management》2020,57(2):102058
Recommender systems are based on inherent forms of social influence. Indeed, suggestions are provided to the users based on the opinions of peers. Given the relevance that ratings have nowadays to push the sales of an item, sellers might decide to bribe users so that they rate or change the ratings given to items, thus increasing the sellers’ reputation. Hence, by exploiting the fact that influential users can lead an item to get recommended, bribing can become an effective way to negatively exploit social influence and introduce a bias in the recommendations. Given that bribing is forbidden but still employed by sellers, we propose a novel matrix completion algorithm that performs hybrid memory-based collaborative filtering using an approximation of Kolmogorov complexity. We also propose a framework to study the bribery effect and the bribery resistance of our approach. Our theoretical analysis, validated through experiments on real-world datasets, shows that our approach is an effective way to counter bribing while, with state-of-the-art algorithms, sellers can bribe a large part of the users. 相似文献
17.
《Information processing & management》2023,60(2):103161
Influence maximization (IM) has shown wide applicability in immense fields over the past decades. Previous researches on IM mainly focused on the dyadic relationship but lacked the consideration of higher-order relationship between entities, which has been constantly revealed in many real systems. An adaptive degree-based heuristic algorithm, i.e., Hyper Adaptive Degree Pruning (HADP) which aims to iteratively select nodes with low influence overlap as seeds, is proposed in this work to tackle the IM problem in hypergraphs. Furthermore, we extend algorithms from ordinary networks as baselines. Results on 8 empirical hypergraphs show that HADP surpasses the baselines in terms of both effectiveness and efficiency with a maximally 46.02% improvement. Moreover, we test the effectiveness of our algorithm on synthetic hypergraphs generated by different degree heterogeneity. It shows that the improvement of our algorithm effectiveness increases from 2.66% to 14.67% with the increase of degree heterogeneity, which indicates that HADP shows high performance especially in hypergraphs with high heterogeneity, which is ubiquitous in real-world systems. 相似文献
18.
The retrieval effectiveness of the underlying document search component of an expert search engine can have an important impact on the effectiveness of the generated expert search results. In this large-scale study, we perform novel experiments in the context of the document search and expert search tasks of the TREC Enterprise track, to measure the influence that the performance of the document ranking has on the ranking of candidate experts. In particular, our experiments show that while the expert search system performance is related to the relevance of the retrieved documents, surprisingly, it is not always the case that increasing document search effectiveness causes an increase in expert search performance. Moreover, we simulate document rankings designed with expert search performance in mind and, through a failure analysis, show why even a perfect document ranking may not result in a perfect ranking of candidate experts. 相似文献
19.
《Journal of The Franklin Institute》2023,360(12):8550-8568
This paper studies the PWM control problem of a class of nonlinear systems. During a modulation period, the PWM control signal maintains a pulse waveform with tunable width and fixed magnitude. The PWM control only possesses finite states, and has relatively limited control capability. This causes the degradation of system performance, and even the instability when implementing into a nonlinear system. We will introduce a novel method to design both the state feedback stabilizer and the output feedback stabilizer for strict-feedback nonlinear systems via the PWM control. The system performance is analyzed in a novel framework and the stability criteria is derived to ensure the system convergence. At last, two examples are considered to illustrate the effectiveness of our proposed method. 相似文献
20.
Social networks are becoming a key communication tool for organizations, but also for top managers like CEOs. Among the different available platforms, Twitter is one of the greatest and it is considered one of the most suitable to share information and engage in dialogue with stakeholders. In this way, this paper analyzes the presence of CEOs on the most active social network sites, and assess the activity and interaction of these top managers on Twitter. CEOs from Global and Latin American companies were selected, to compare their performance. The results of the study show that the presence of CEOs in social networks is very low, and the majority of those that are present on them are not adequately using their Twitter accounts. Although the general presence and performance on are low, LatAm CEOs have a better presence on social networks and they are more active on Twitter, but Global CEOs have better interaction results on their accounts. So, this area of strategic communication should be improved by communication practitioners, since the CEO communication is nowadays a key communication issue for any organization. 相似文献