Aug 2026· Finance and Society· pp. 1-25· 0 citations· 44 references
Abstract
This article examines how social trading platforms encourage laypeople to become active investors and traders and to deepen their engagement over time. Extending prior research highlighting the role of cultural narratives and platform design, we show how platforms steer users along engagement pathways, transitioning novices into active investors and traders, and ultimately into co-creators, who act as finfluencers and/or provide quasi-fund management. Using a walkthrough method on two major platforms, eToro and Binance, we identify five key elements that shape these pathways: (1) the educational ‘Academy’, which establishes a market ontology; (2) monetary reward systems, which define valued activities; (3) user profile designs, which script specific roles; (4) social reward systems, which guide interactions; and (5) social feed algorithms, which curate user experience. Our comparative analysis shows how these elements are strategically aligned to steer users towards two distinct subjectivities. eToro cultivates an ‘investing-oriented’ pathway focused on building trust for its copy trading feature, while Binance fosters a ‘trading-oriented’ pathway centred on generating content and attention. Our findings contribute to the sociology of finance by detailing the socio-material mechanisms that create and sustain lay investor engagement, and to the literature on consumer co-creation by showing how platforms systematically configure users into valuable producers.
In Bangladesh’s rapidly expanding informal e-market, small-scale sellers use social media platforms like Facebook to run businesses outside formal infrastructures. These sellers rely heavily on platform algorithms—not just for visibility, but as active collaborators in business operations. Drawing on 41 in-depth interviews with sellers, buyers, and stakeholders, this paper examines how people in informal e-market perceive and interact with the algorithm as a “team member" that performs sales, marketing, and customer engagement tasks. We found that while sellers and local tech entrepreneurs are intrigued to develop services to support this industry, buyers and investors of the industry put their greater trust in human interactions. This surfaces a postcolonial tension associated with cultural values, local tech education and training, and a mismatch between the global and Bangladeshi e-markets’ growth. We expand this discussion from multiple ongoing HCI, political design, and AI design angles. We also address the postcolonial tension and support the decoloniality movement in informal e-markets by proposing the DAIEM framework that consists of six components: autonomy and agency; resistance; locality, culture, and history; rationality; materiality; and advocacy. Supporting the decoloniality and informality sentiment in informal e-market and other similar sectors, DAIEM will serve both as a guideline for algorithm design and as an analytical tool.
Social media recommendation systems create conflict: content producers, content consumers, platform operators, and social pressures struggle to direct the allocation of attention in their favor while also facing competing societal pressures. Producers may feel pressure to optimize for recommendation algorithms and consumers may be exposed to content with negative externalities such as polarization and misinformation. Platforms aim to maximize user engagement, oftentimes resulting in over consumption from consumers. There is mounting pressure from policymakers and broader society to address these issues. Prior methods for constructing social media feeds have focused on recommendation systems. Instead, we propose a, to our knowledge, novel auction based method for feed construction where users bid for the attention of other users. Our mechanism systematically considers producers, consumers, platform operators, and social welfare. We show that our auction is weakly incentive compatible under budget constraints. To balance between producer and consumer welfare, we introduce a tax policy to the auction to increase the cost of content with negative externalities. Simulations over common social network topologies and an empirically observed network show how different feed algorithms prioritize different stakeholders. Our proposed auction based mechanisms produce on average 36.3% higher producer welfare than comparison algorithms on the empirically observed network and 31.4% higher producer welfare than comparison algorithms on the synthetic networks. Our methods also produce more equitable distributions of attention than baseline methods across all evaluated network types. Our proposed mechanism addresses attention allocation at a systematic level, balancing between the needs of different stakeholders.
