From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets

Saura, José Ramón; Soriano, Domingo Ribeiro; Palacios‐Marqués, Daniel

doi:10.1016/j.ijinfomgt.2021.102331

Cited by 152 publications

(110 citation statements)

References 110 publications

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“…In recent years, the strategy of focusing on data-driven innovation has led to new products and business models in the emerging and developing digital markets. However, while exploring knowledge from data, user privacy is an issue that needs to be treated with caution [75,76].…”

Section: Managerial Implicationmentioning

confidence: 99%

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

Chao

Trappey

2021

Complexity

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Natural language processing (NLP) is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including document term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals.

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Section: Managerial Implicationmentioning

confidence: 99%

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

Chao

Trappey

2021

Complexity

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“…Therefore, one practical implication of the present findings is that using self-photographs as profiles on social media may be a notable feature of the internet users with better mental health status. However, as noted by Saura and colleagues [ 6 , 35 ], user data should be collected and analyzed in a way that does not invade the privacy of users and follow social media ethics.…”

Section: Discussionmentioning

confidence: 99%

Profile Pictures in the Digital World: Self-Photographs Predict Better Life Satisfaction

Hung

Lee

Peng

2021

IJERPH

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Profiles pictures as online identities represent an extension of the user's self in the digital world. Changes in self-representation are responsible for reduced well-being in individuals in the offline world. However, whether profile picture selection predicts the well-being of internet users is unknown. To address this question, we tested the relationship between the type of profile picture (e.g., self-photographs or other pictures) used on social media and the life satisfaction of internet users, accounting for gender and personality traits that have been thought to relate to the selection of profile pictures. The results showed that individuals using self-photographs as profiles reported a higher level of life satisfaction compared to individuals using other pictures as profiles. This effect was influenced by gender, openness, and extraversion. Hierarchical regression and moderation analyses revealed that openness and profile type interacted to predict life satisfaction in women, while openness and profile picture independently predicted life satisfaction in men. Furthermore, extraversion directly predicted life satisfaction in both men and women. These results indicate that the consistency between one's online and offline self-representation may characterize internet users' well-being, with potential implications for digital wellness.

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“…For example, the comprehensive survey in [3] showed that, among other uses, data science can be exploited to identify and prevent fake news in digital marketing, thus ruling out false content about companies and products and helping customers avoid predatory commercial campaigns [3]. Given the large impact that data and genuine user behavior have on many processes, including data-driven innovation [5], preserving a healthy online interaction environment is extremely important. Among other consequences, this may reduce concerns about the use of hyper-connected technologies, increasing trust and enabling the widespread deployment of high value-added services, as recently seen in contact tracing applications for pandemic tracking and prevention [6].…”

Section: Related Workmentioning

confidence: 99%

“…Not only does social networking offer the chance to exchange opinions, information, discussion, as well as multimedia contents: it is also an invaluable source of data [3]. Unhealthy online social behaviors can damage this source by working as echo chambers of toxic contents and may reduce the trust of the users in social media by posing privacy breaching concerns [5,6].…”

Section: Introductionmentioning

confidence: 99%

Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

2021

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The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

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From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets

Cited by 152 publications

References 110 publications

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

Profile Pictures in the Digital World: Self-Photographs Predict Better Life Satisfaction

Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

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