Information Quality and Social Networks

PurposeThe paper investigates the role of social networks in the millennials’ decision-making process of illegal and unnotified food supplements purchase. The connections and interactions that (co) produce information are studied with a holistic perspective of social sustainability as a development driver of business model innovation.Design/methodology/approachAn exploratory qualitative multiple analysis study was conducted in two consecutive phases. Data from 23 semi-structured individual interviews were collected, followed by a netnographic analysis of the Facebook virtual community.FindingsThe results show that the decision-making process does not develop following the traditional sequence, as social networks modify the wellness meaning creation process and reduce risk perception. Moreover, social networks introduce the use of similar experiences of others and online information and emotional support on unethical and unhealthy behavior.Research limitations/implicationsDue to the application to a social network, the results should be understood within this context. Future studies would benefit by expanding the target and the range of social networks explored.Practical implicationsThe official information quality control, as a prerogative of public and professional health stakeholders, and the medialization of medicalization, contribute to the conscious development of their wellness meanings and values.Originality/valueThis work represents one of the first attempts to investigate resources integration through social networks in the pre-purchase decision-making process of unnotified and illegal food supplements. Unethical and unhealthy behavior develops through the interaction of actors, firms, influencers and individuals over social networks.

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Information quality, privacy policies, and data safety practices in online social networks for health: A longitudinal analysis

Proceedings of the Association for Information Science and Technology ◽

10.1002/pra2.2015.145052010058 ◽

2015 ◽

Vol 52 (1) ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Kaitlin L. Costello

Keyword(s):

Social Networks ◽

Longitudinal Analysis ◽

Online Social Networks ◽

Information Quality ◽

Privacy Policies ◽

Safety Practices ◽

Data Safety

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Information Quality in Online Social Networks: A Fast Unsupervised Social Spam Detection Method for Trending Topics

Proceedings of the 19th International Conference on Enterprise Information Systems ◽

10.5220/0006372006630675 ◽

2017 ◽

Cited By ~ 1

Author(s):

Mahdi Washha ◽

Dania Shilleh ◽

Yara Ghawadrah ◽

Reem Jazi ◽

Florence Sedes

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Information Quality ◽

Detection Method ◽

Spam Detection ◽

A Survey and a Preliminary Evaluation of Low-quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?

10.5753/kdmile.2019.8784 ◽

2019 ◽

Author(s):

Júlio Resende ◽

Igor Moraes ◽

Nícollas Silva ◽

Vinícius Durelli ◽

Diego Dias ◽

...

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Information Quality ◽

Preliminary Evaluation ◽

Quality Detection ◽

Wide Range ◽

Different Types ◽

Current Scenario ◽

Detection Strategies

Online social networks have gone mainstream: millions of users have come to rely on the wide range of services provided by social networks. However, the ease use of social networks for communicating information also makes them particularly vulnerable to social spammers, i.e., ill-intentioned users whose main purpose is to degrade the information quality of social networks through the proliferation of different types of malicious data (e.g., social spam, malware downloads, and phishing) that are collectively called low-quality content or spams. Since Twitter is also rife with low-quality content, several researchers have devised various low-quality detection strategies that inspect tweets for the existence of spam contents. We carried out a literature survey of these low-quality detection strategies, examining which strategies are still applicable in the current scenario – taken into account that Twitter has undergone a lot of changes in the last few years. To gather some evidence of the usefulness of the attributes used by the low-quality detection strategies, we carried out a preliminary evaluation of these attributes.

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