A social network is an umbrella term encompassing all the web-based applications that let individuals generate content and foster active collaboration, participation and exchange of information [1]. Indeed, the effective use of these collaboration tools has become essential to business success [2].
Social media governance and privacy concerns
One drawback of these social platforms is their centralisation. A single proprietary organisation exerts centralised control over the network. This feature gives rise to critical trust and governance issues for content creation and propagation in those environments. Especially concerning is that centralised intermediaries are involved in regular data breaches.
Recently, Eurobarometer surveyed in March 2020 the attitudes towards the impact of digital technologies and environment, sharing personal information, disinformation and digital skills on the daily lives of Europeans [3]. This EU survey revealed that nearly 60% of respondents are willing to share their personal information securely to improve public services. This survey raises huge concerns on privacy in traditional social media, where users are typically giving away their personal data with a simple click, sold to the highest bidder. The Eurobarometer survey also discovered that 71% of respondents encountered fake news several times a month or more. They believe that media should be responsible for combating disinformation, followed by public authorities and social media platforms [3].
People are increasingly reliant on social media networks. Unfortunately, liking, sharing, and searching for information allows social bots (i.e. automated accounts impersonating humans) to magnify the spread of fake news [4]. Fake news spreads much faster than scientifically proven findings. Motivated efforts reduce the capacity of science potential and encourage the spread of false information. Mixing advisory groups with individuals who represent private rather than public interests can obfuscate the truth, too [5].
Infodemic in the social media
Digital data is highly transmissible and circulates through the world’s most heavily used information ecosystems, such as social media. The worldwide coronavirus pandemic (COVID-19) has drastically changed our lives, mainly how we interact, socialise, and work with others. Nonetheless, social media fraud has increased COVID-19 pandemic rumours and other types of threatening digital medical material. The new pandemic and associated containment measures have heightened the infodemic, a phenomenon in which people disseminate and propagate false or misleading information via interconnected digital networks.
The infodemic has proven that information from social media platforms spreads quite rapidly. Social media platforms shape and mobilise communication patterns, public opinion practices of exchange, businesses, creation, learning, and knowledge acquisition. ARTICONF offers an intelligent social media ecosystem in a blockchain federated environment [6], supported by an underlying blockchain technology seamlessly coupled with optimised trust-based measures in an anonymised environment. ARTICONF simplifies traceability to identify bad actors, malicious contents and disinformation. The data-driven analysis supported by a decentralised back-end infrastructure such as the ARTICONF can help counter fake news. These decision-making methodologies engage community experts, which enable efficient and trustworthy content management, to confront the infodemic across social networks. For this reason, decentralisation can bring reliability and security into the entire process.
Data-driven analytics tool
The Tools for Analytics and Cognition (TAC) are intelligent data visualisation software. This analytic system is responsible for collecting, aggregating and analysing data, producing meaningful insights for users using visual formats. The choice for visualisation is a sort of “business intelligence” that helps parse large amounts of data and then presents them in a new way to facilitate understanding and decision making. The goal is to provide meaningful insights from the data. Such representations allow fast information transfer from the machine to the human brain efficiently and in the most meaningful manner possible. Therefore, the visualisation adds an aesthetic value and emphasises the message’s clarity but requires data-sets to have trustworthy content.
Data analytics takes a step further in detecting or uncovering patterns and trends from the collected data. Users can make sense of data with visualisations. By providing aggregation, monitoring, cognitive reasoning and learning modules that analyse the behaviour and engagement of the application and social media actors, ARTICONF helps diagnose performance risks and provides guided analytics to consumers, prosumers and application providers to improve collaboration and revenues.
Finally, data visualisation depicts data in a visual context by highlighting the data’s intrinsic trends and patterns. Text-based data may not reveal such patterns and trends at the same speed. Most programs allow users to apply filters to data according to their needs. Moreover, perceptive 3D dashboard representations supplement charts, tables, line graphs, and other classic visualisations, enabling better predictive analytics, planning, and observing social media marketing strategy’s effect on the audience. It also allows providers to determine the impact of integrating social media services and the overall application performance.
An innovative metric for collaboration and revenue growth
The newly defined Return on Collaboration (ROC) metric allows decentralised application providers to follow the growth of new active users engaged with their ARTICONF powered platforms [6]. ROC’s dashboard visualisation can bring additional insights on predictive analytics, planning, and observing social media infodemics [7]. TAC estimates stakeholders’ channel monetisation by measuring user engagement with trusted content. TAC provides visualisation insights for stakeholders to choose the right track for misinformation [8].
Importance of crowd journalism in fighting infodemics
The crowd journalism use case in TAC context provides qualitative support for the analytic system, injecting additional information to enhance the crowd journalism communities’ operational tasks, planning, and management. TAC uses visualisations to measure the user’s engagement rate, monitor the Return of Investment (ROI), diagnose investment risks for social media providers, and improve collaboration and revenue. The ROI measures in the context of social networking are still evolving. Some methods can determine progress and cooperation. The innovative ROC metric, implemented by a ROC microservice, helps crowd journalism providers track the application network’s spread with new active users engaged with the ARTICONF platform. TAC’s visualisation insights for crowd journalism can bring extra revenue to businesses and determine the right track for further capital investments [9].
Diverse social applications from different (especially developing) countries can join in such a decentralised platform and enjoy the advantages of using the virtual blockchain infrastructure, offering increased autonomy and data privacy and trustiness from applying the smart-context algorithms. A customised guided analytics dashboard drives their business decisions. The ARTICONF Horizon 2020 project addresses trust issues, time-criticality and democratisation for a new generation of federated infrastructure to fulfil the privacy, robustness, and autonomy related promises that proprietary social ecosystems have failed to deliver so far.
References
[1] J. K. Waters, “The Everything Guide to Social Media: All You Need to Know About Participating in Today’s Most Popular Online Communities”, Adams Media, 2010.
[2] C. Wiese, “The Return on Collaboration: Assessing the Value of Today’s Collaboration Solutions”, Cisco Public Information, 2010, https://tec4it.com/wp-content/uploads/2015/10/Case-study_Return_on_collab_wp.pdf.
[3] J. Bahkre and C. Manory, “Shaping Europe’s digital future: Eurobarometer survey shows support for sustainability and data sharing”, European Commission, March 2020, https://ec.europa.eu/commission/presscorner/detail/en/IP_20_383.
[4] D. M. J. Lazer, M. A. Baum, Y. Benkler, A. J. Berinsky, K. M. Greenhill, F. Menczer, M. J. Metzger, B. Nyhan, G. Pennycook, D. Rothschild, M. Schudson, S. A. Sloman, C. R. Sunstein, E. A. Thorson, D. J. Watts, and J. L. Zittrain, “The science of fake news”, Science, 359(6380):1094–1096, March 2018, DOI: 10.1126/science.aao2998.
[5] L. W. Green, R. C. Brownson, and J. E. Fielding, “Introduction: fake news, science, and the growing multiplicity and duplicity of information sources”, Annual Review of Public Health, 41(1):v–vii, April 2020, DOI: 10.1146/annurev-pu-41-012720-100001.
[6] R. Prodan, N. Saurabh, Z. Zhao, K. Orton-Johnson, A. Chakravorty, A. Karadimce, A. Ulisses, “ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment”, Euro-Par 2019: Parallel Processing Workshops, LNCS 11997, pp. 417–428, DOI: 10.1007/978-3-030-48340-1_32.
[7] M. Sefidanoski, A. Hristov, A. Karadimce, N. Paunkoska Dimoska, N. Marina, “Social Data Analytics for Enhancing the Collaborative Economy”, 5th International Symposium on Multidisciplinary Studies and Innovative Technologies, IEEE, October 21-23, 2021, Turkey DOI: 10.1109/ISMSIT52890.2021.9604730.
[8] N. Paunkoska (Dimoska), A. Hristov, A. Karadimce, N. Marina, M. Sefidanoski, “Return on Collaboration and Social Media Revenue”, ARTICONF Blog 2021, https://articonf.eu/return-on-collaboration/.[9] N. Paunkoska Dimoska, A. Hristov, A. Karadimce, N. Marina, M. Sefidanoski, “Tools for Analytics and Cognition for Crowd Journalism Application”, 13th EAI International Conference on e-Infrastructure and e-Services for Developing Countries, December 2021.
This blog post was written by the University of Information Science and Technology “St. Paul the Apostle” – Ohrid team in January 2022.
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