Recommendations in Academic Social Media: the shaping of scholarly communication through algorithmic mediation

Scholarly communication is increasingly being mediated by Academic Social Media (ASM) platforms, which combine the functions of a scientifi c repository with social media features such as personal profi les, followers and comments. In ASM, algorithmic mediation is responsible for fi ltering the content and distributing it in personalised individual feeds and recommendations according to inferred relevance to users. However, if communication among researchers is intertwined with these platforms, in what ways may the recommendation algorithms in ASM shape scholarly communication? Scientifi c literature has been investigating how content is mediated in data-driven environments ranging from social media platforms to specifi c apps, whereas algorithmic mediation in scientifi c environments remains neglected. This thesis starts from the premise that ASM platforms are sociocultural artefacts embedded in a mutually shaping relationship with research practices and economic, political and social arrangements. Therefore, implications of algorithmic mediation can be studied through the artefact itself, peoples’ practices and the social/political/ economic arrangements that aff ect and are aff ected by such interactions. Most studies on ASM focus on one of these elements at a time, either examining design elements or the users’ behaviour on and perceptions about such platforms. In this thesis, a multifaceted approach is taken to analyse the artefact as well as the practices and arrangements traversed by algorithmic mediation. Chapter 1 reviews the literature about ASM platforms, and explains the history of algorithmic recommendations, starting from the fi rst Information Retrieval systems to current Recommender Systems, highlighting the use of diff erent data sources and techniques. The chapter also presents the mediation framework and how it applies to ASM platforms, before outlining the thesis. The rest of the thesis is divided in two parts. Part I focuses on how recommender systems in ASM shape what users can see and how users interact with and through the platform. Part II investigates how, in turn, researchers make sense of their online interactions within ASM. The end of Chapter 1 shows the methodological choices for each following chapter. Part I presents a case study of one of the most popular ASM platforms in which a walkthrough method was conducted in four steps (interface analysis, web code inspection, patent analysis and company inquiry using the General Data Protection Regulation (GDPR)). In Chapter 2 it is shown that almost all the content in ASM platforms are algorithmically mediated through mechanisms of profi ling, information selection and commodifi cation. It is also discussed how the company avoids explaining the workings of recommender systems and the mutually shaping characteristic of ASM platforms. Chapter 3 explores the distortions and biases that ASM platforms can uphold. Results show how profi ling, datafi cation and prioritization have the potential to foster homogeneity bias, discrimination, the Matthew eff ect of cumulative advantage in science and other distortions. Part II consists of two empirical studies involving participants from diff erent countries in interviews (n=11) and a research game (n=13). Chapter 4 presents the interviews combined with the show and tell technique. The results show the participant’s perceptions on ASM aff ordances, that revolve around six main themes: (1) getting access to relevant content; (2) reaching out to other scholars; (3) algorithmic impact on exposure to content; (4) to see and to be seen; (5) blurred boundaries of potential ethical or legal infringements, and (6) the more I give, the more I get. We argue that algorithmic mediation not only constructs a narration of the self, but also a narration of the relevant other in ASM platforms, confi guring an image of the relevant other that is both participatory and productive. Chapter 5 presents the design process of a research game and the results of the empirical sessions, where participants were observed while playing the game. There are two outcomes for the study. First, the human values researchers relate to algorithmic features in ASM, the most prominent being stimulation, universalism and self-direction. Second, the role of the researcher’s approach (collaborative, competitive or ambivalent) in academic tasks, showing the consequential choices people make regarding algo- rithmic features and the motivations behind those choices. The results led to four archetypal profi les: (1) the collaborative reader; (2) the competitive writer; (3) the collaborative disseminator; and (4) the ambivalent evaluator. The fi nal chapter summarises the ways in which ASM platforms forges people’s perceptions and the strategies people employ to use the systems in benefi t of their careers, answering each research question. Chapter 6 discusses the implications of algorithmic mediation for scholarly communication and science in general. The dissertation ends with refl ections on human agency in data-driven environments, the role of algorithmic inferences in science and the challenge of reconciling individual user’s needs with broader goals of the scientifi c community. By doing so, the contribution of this thesis is twofold, (1) providing in-depth knowledge about the ASM artefact, and (2) unfolding diff erent aspects of the human perspective in dealing with algorithmic mediation in ASM. Both perspectives are discussed in light of social arrangements that are mutually shaped by artefact and practices.A comunicação acadêmica é cada vez mais mediada por plataformas de Mídia Social Acadêmica (MSA), que combinam as funções de um repositório científi co com recursos de mídia social, como perfi s pessoais, seguidores e comentários. Nas MSA, a mediação algorítmica é responsável por fi ltrar o conteúdo e distribuí-lo em feeds e recomendações individuais personalizados de acordo com a relevância inferida para os usuários. No entanto, se a comunicação entre pesquisadores está entrelaçada com essas plataformas, de que forma os algoritmos de recomendação nas MSA podem moldar a comunicação acadêmica? A literatura científi ca vem investigando como o conteúdo é mediado em ambientes orientados por dados, desde plataformas de mídia social até aplicativos específi cos, enquanto a mediação algorítmica em ambientes científi cos permanece negligenciada. Esta tese parte da premissa de que as plataformas de MSA são artefatos socioculturais inseridos em uma relação mutuamente modeladora com práticas de pesquisa e arranjos econômicos, políticos e sociais. Portanto, as implicações da mediação algorítmica podem ser estudadas através do próprio artefato, das práticas humanas e dos arranjos sociais/políticos/ econômicos que afetam e são afetados por tais interações. A maioria dos estudos sobre MSA se concentra em um desses elementos de cada vez, seja examinando elementos de design ou o comportamento e percepções dos usuários sobre essas plataformas. Nesta tese, uma abordagem multifacetada é feita para analisar o artefato, bem como as práticas e arranjos atravessados pela mediação algorítmica. O Capítulo 1 revisa a literatura sobre plataformas de MSA e explica a história das recomendações algorítmicas, desde os primeiros sistemas de Recuperação de Informação até os atuais Sistemas de Recomendação, destacando o uso de diferentes fontes de dados e técnicas. O capítulo também apresenta o quadro teórico (mediation framework) e como ele se aplica às plataformas MSA, antes de delinear a estrutura da tese. O restante da tese está dividido em duas partes. A Parte I se concentra em como os sistemas de recomendação nas MSA moldam o que os usuários podem ver e como os usuários interagem com e na plataforma. A Parte II, por sua vez, investiga como os pesquisadores dão sentido às suas interações online dentro das MSA. O fi nal do Capítulo 1 mostra as opções metodológicas para cada capítulo seguinte. A Parte I apresenta um estudo de caso de uma das plataformas de MSA mais populares em que o walkthrough method foi realizado em quatro etapas (análise de interface, inspeção de código web, análise de patente e consulta à empresa usando o General Data Protection Regulation (GDPR)). No Capítulo 2 é mostrado que quase todo o conteúdo das plataformas ASM é mediado por algoritmos por meio de mecanismos de perfi - lamento, seleção de informações e mercantilização. Também é discutido como a empresa evita explicar o funcionamento dos sistemas de recomendação e a característica de modelagem mútua das plataformas de MSA. O Capítulo 3 explora as distorções e vieses que as plataformas de MSA podem sustentar. Os resultados mostram como o perfi lamento, a datifi cação e a priorização de conteúdo têm o potencial de promover viés de homogeneidade, discriminação o efeito Mateus de vantagem cumulativa na ciência e outras distorções. A Parte II consiste em dois estudos empíricos envolvendo participantes de diferentes países em entrevistas (n=11) e um jogo de pesquisa (n=13). O capítulo 4 apresenta as entrevistas combinadas com a técnica show and tell. Os resultados mostram as percepções dos participantes sobre as aff ordances das MSA, que giram em torno de seis temas principais: (1) ter acesso a conteúdos relevantes; (2) acesso a outros pesquisadores; (3) impacto algorítmico na exposição ao conteúdo; (4) ver e ser visto; (5) limites difusos de potenciais infrações éticas ou legais e (6) quanto mais eu dou, mais eu recebo. Argumentamos que a mediação algorítmica não apenas constrói uma narração do eu, mas também uma narração do outro nas plataformas de MSA, confi gurando uma imagem do outro ao mesmo tempo participativa e produtiva. O capítulo 5 apresenta o processo de design de um jogo de pesquisa e os resultados das sessões empíricas, onde os participantes foram observados enquanto jogavam o jogo. Há dois resultados para o estudo. Primeiro, quais valores humanos os pesquisadores relacionam com recursos algorítmicos nas MSA, sendo os mais proeminentes o estímulo, o universalismo e o autodirecionamento. Em segundo lugar, o papel da abordagem do pesquisador (colaborativa, competitiva ou ambivalente) em tarefas acadêmicas, mostrando as escolhas consequentes que as pessoas fazem em relação aos recursos algorítmicos e as motivações por trás dessas escolhas. Os resultados levaram a quatro perfi s arquetípicos: (1) o leitor colaborativo; (2) o escritor competitivo; (3) o divulgador colaborativo; e (4) o avaliador ambivalente. O capítulo fi nal (Capítulo 6) resume as maneiras pelas quais as plataformas de MSA forjam as percepções das pessoas e as estratégias que as pessoas empregam para usar os sistemas em benefício de suas carreiras, respondendo a cada questão de pesquisa. O capítulo discute ainda as implicações da mediação algorítmica para a comunicação acadêmica e a ciência em geral. A dissertação termina com refl exões sobre a agência humana em ambientes orientados por dados, o papel das inferências algorítmicas na ciência e o desafi o de conciliar as necessidades individuais do usuário com os objetivos mais amplos da comunidade científi ca. Ao fazê-lo, a contribuição desta tese é dupla, (1) fornecendo conhecimento aprofundado sobre o artefato plataformas de MSA, e (2) desdobrando diferentes aspectos da perspectiva humana ao lidar com mediação algorítmica em ASM. Ambas as perspectivas são discutidas à luz de arranjos sociais que são mutuamente moldados por artefatos e práticas

Controlling the Crucible : A Novel PvP Recommender Systems Framework for Destiny

Compared to conventional retail games, today's Massively Multiplayer Online Games (MMOGs) have become progressively more complex and volatile, living in a highly competitive market. Consumable resources in such games are nearly unlimited, making decisions to improve levels of engagement more challenging. Intelligent information filtering methods here can help players make smarter decisions, thereby improving performance, increasing level of engagement, and reducing the likelihood of early departure. In this paper, a novel approach towards building a hybrid multi-profile based recommender system for player-versus-player (PvP) content in the MMOG Destiny is presented. The framework groups the players based on three distinct traced behavioral aspects: base stats, cooldown stats, and weapon playstyle. Different combinations of these profiles are considered to make behavioral recommendations. An online evaluation was performed to investigate the usefulness of the proposed recommender framework to players of Destiny

Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems

Recent academic research has extensively examined algorithmic collusion resulting from the utilization of artificial intelligence (AI)-based dynamic pricing algorithms. Nevertheless, e-commerce platforms employ recommendation algorithms to allocate exposure to various products, and this important aspect has been largely overlooked in previous studies on algorithmic collusion. Our study bridges this important gap in the literature and examines how recommendation algorithms can determine the competitive or collusive dynamics of AI-based pricing algorithms. Specifically, two commonly deployed recommendation algorithms are examined: (i) a recommender system that aims to maximize the sellers' total profit (profit-based recommender system) and (ii) a recommender system that aims to maximize the demand for products sold on the platform (demand-based recommender system). We construct a repeated game framework that incorporates both pricing algorithms adopted by sellers and the platform's recommender system. Subsequently, we conduct experiments to observe price dynamics and ascertain the final equilibrium. Experimental results reveal that a profit-based recommender system intensifies algorithmic collusion among sellers due to its congruence with sellers' profit-maximizing objectives. Conversely, a demand-based recommender system fosters price competition among sellers and results in a lower price, owing to its misalignment with sellers' goals. Extended analyses suggest the robustness of our findings in various market scenarios. Overall, we highlight the importance of platforms' recommender systems in delineating the competitive structure of the digital marketplace, providing important insights for market participants and corresponding policymakers.Comment: 33 pages, 5 figures, 4 table

Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governance

Divisiveness appears to be increasing in much of the world, leading to concern about political violence and a decreasing capacity to collaboratively address large-scale societal challenges. In this working paper we aim to articulate an interdisciplinary research and practice area focused on what we call bridging systems: systems which increase mutual understanding and trust across divides, creating space for productive conflict, deliberation, or cooperation. We give examples of bridging systems across three domains: recommender systems on social media, collective response systems, and human-facilitated group deliberation. We argue that these examples can be more meaningfully understood as processes for attention-allocation (as opposed to "content distribution" or "amplification") and develop a corresponding framework to explore similarities - and opportunities for bridging - across these seemingly disparate domains. We focus particularly on the potential of bridging-based ranking to bring the benefits of offline bridging into spaces which are already governed by algorithms. Throughout, we suggest research directions that could improve our capacity to incorporate bridging into a world increasingly mediated by algorithms and artificial intelligence.Comment: 40 pages, 11 figures. See https://bridging.systems for more about this wor

Personalized Game Content Generation and Recommendation for Gamified Systems

Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive must be reinforced to lead to stabilization. This issue could be sourced from utilizing static game content and a one-size-fits-all strategy in generating the content during the game. This reveals the need for dynamic personalization over the course of the game. Our research hypothesis is that we can overcome these limitations with Procedural Content Generation (PCG) of playable units that appeal to each individual player and make her user experience more varied and compelling. In this thesis, we propose a deep, large and long solution, deployed in two main phases of Design and Integration to tackle these limitations. To support the former phase, we present a “PCG and Recommender system” to automate the generation and recommendation of playable units, named “Challenges”, which are Personalized and Contextualized on the basis of players’ preferences, skills, etc., and the game ulterior objectives. To this end, we develop a multi-layered framework to generate the personalized game content to be assigned and recommended to the players involved in the gamified system. To support the latter phase, we integrate two modules into the system including Machine Learning (ML) and Player Modeling, in order to optimize the challenge selection process and learning players’ behavior to further improve the personalization, by deriving the style of the player, respectively. We have carried out the implementation and evaluation of the proposed framework and its integration in two different contexts. First, we assess our Automatic Procedural Content Generation and Recommendation (APCGR) system within a large-scale and long-running open field experiment promoting sustainable urban mobility that lasted twelve weeks and involved more than 400 active players. Then, we implement the “Player Modeling” module (in the integration phase) in an educational interactive game domain to assess the performance of the proposed play style extraction approach. The contributions of this dissertation are a first step toward the application of machine learning in automating the procedural content generation and recommendation in gamification systems