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Social networks are widely recognized as a new communication technology that has transformed social contexts (Bohlin et al., 2018). For example, banks have exploited this technology using mainly Facebook, Twitter, and YouTube for marketing, financial advice, information support, customer service, sales, surveys, polls, and other services (Mabic et al., 2017). Thus, services offered through mobile devices and social networks are disruptive because they can replace other banking channels and make branch-oriented banking redundant (Boyd & Ellison, 2007; Miranda et al., 2013).
Users of social networks and e-commerce websites express their opinions or comments on banks' products and services, which is used to analyze service quality perceptions, which improves responses to maintain loyalty and trust (Nicoletti, 2017). Advances in the development of algorithms allow measuring interactivity and content posted about a given bank, taking advantage of the potential of social networks to identify stakeholders and learn about their needs, expectations, interests, preferences, and opinions; however, the integration of services with social networks is not an easy task.
Technological advances have increased its subsidiaries' management and control capacity (Berger & DeYoung, 2005). Since 1992, when some USA banks started using electronic banking services, the Internet and social networks became the industry's backbone (Mucan & Özelturkay, 2014). These developments have enabled the industry to promote an organizational identity, develop public relations with stakeholders, solicit feedback, share content, or analyze conversations to handle complaints and grievances to create new solutions (Keskar & Pandey, 2018; Ozdora & Atakan, 2016; Schulte, 2018).
According to Bohlin et al. (2018), banks that are pioneers in innovation use social networks, thus revealing their best practices; therefore, social networks become one of the main channels for managing reputational risk (Porras & Orozco, 2019). For example, if there is a concentration of liquidity among a small group of banks and this spreads in social networks, the collapse may be greater, and the whole network may collapse (Leitner, 2005) so that the defaults of one bank could influence the failure of another in a domino effect (Ozdora & Atakan, 2016). Uhde & Michalak (2010) securitization of credit risk has a positive impact on increasing the systematic risk of European and Swiss banks as they seek financial leverage, achieving standards such as the publication of their securitizations to control expectations of external investors and bank managers.
Information about the banking sector structure using the interbank network topology is a valuable tool for directing policies, managing contagion risk, and preventing financial system failures (Boss et al., 2004; Houston et al., 2018; Leitner, 2005). For instance, Elsinger et al. (2006) showed that contagion scenarios among Austrian banks would be a rare event; however, such scenarios can occur when there are many fundamental defaults due to the exposure of correlated portfolios affecting most of the banking sector. In such a case, 12% of the banking system's total securities would have to be added to recover from a fundamental default and 1% for a systematic risk of contagion.
The analysis of social networks such as Twitter uses Bayesian risk models that can predict a bank's failures and default conditional on the information disseminated by its network (Cerchiello et al., 2017); models to identify and classify stakeholders and define the structure of the banking sector using social network analysis with opinions and sentiments in networks are scarce in the literature. Keskar et al. (2020) determined the main features of a network model for creating an index to assist banks in achieving customer satisfaction on the Internet.