Location-based media applications such as Google Maps, Strava and Pokémon GO together have more than a billion monthly active users, and popular social media such as Snapchat and Instagram now also feature map-based content. All these media products rely on user-generated content as a core element of their service, but there is a lack of synthesis on the users' motivations to contribute this data to the platform providers. In this study, we performed a literature review to uncover users' motivations to participate in location-based crowdsourcing and contribute shared content on these platforms. Among our findings, we show that spatial and temporal aspects, social effects, technical elements, motivational mechanisms, practical value offered to the contributors and individual differences need to be considered in motivating users to contribute shared content. We present recommendations for designers, suggest which terminology to use around this topic and propose an agenda for future research.

Advances in mobile consumer technology, especially in smartphones, have given rise to numerous new crowdsourcing opportunities. With the miniaturization of sensors and the growing number of smartphone models containing a wide range of them, we are now capable of creating digital imitations of real-world objects using everyday mobile devices. In this study, we created four augmented reality (AR) applications utilizing LiDAR sensors that were designed for crowdsourcing the creation of such digital imitations, i.e., digital twins. A user study was undertaken where video recordings, audio recordings, point cloud, and mesh data were collected in forest settings. Each approach was evaluated in terms of the characteristics and quality of the crowdsourced data acquired, as well as the participants’ behavior and experience. Our findings demonstrate that through gamification approaches, we can influence not only the user experience but also the type and quality of the crowdsourced data. Our findings offer guidance on which gamification dynamics and interactions are best suited for several types of crowdsourcing tasks. We also show that the collection of meaningful data is not limited to specific interactions or experiences and suggest that there are multiple routes in gamification design for reaching the desired outcomes.

Remote sensing for observing and recording our surroundings is becoming mainstream. Technologies, such as light, detection, and ranging (LiDAR), are now part of consumer mobile devices and provide a variety of novel interaction opportunities with the environment. Mobile remote sensing also provides affordances for crowdsourcing through location-based applications such as games and gamified systems. While such use cases today are technologically feasible, there is a lack of understanding of how and what kinds of interactions and applications would be both (1) engaging and motivating for users and also (2) maximize the volume and quality of the data being gathered. In this study, we investigate these challenges by developing and testing four gamified augmented reality prototypes that use LiDAR for collecting point cloud data during location-based gaming. Through field testing, interviews, and surveys with 21 participants, followed by reflexive thematic analysis, we identified five themes of dynamics, which exemplify tensions and challenges to designing gamified AR crowdsourcing. The findings primarily point to hazards in design that may undermine user motivation as well as constraints of the environments themselves in facilitating and affording meaningful and rich (gameful) interaction.

There are numerous benefits from regularly walking in nature, and today's mobile technologies have the potential to encourage people to do so. Past research has showed that gamified map-based apps and location-based games (LBGs) have the capability to incentivize people to go to nature areas in cities and beyond. In this study, we explored LBGs' potential to bring people to nature by conducting a field experiment with a new mechanic called Routes in the popular LBG Pokémon GO. Prior to the Route feature's launch, we created altogether 13 Routes of various lengths in both city and nature landscapes. We collected numerical in-game data of how many times each Route was walked and deployed a survey (n=67) for Pokémon GO players in the area where the Routes were made. The findings suggest that proximity to population concentrations and in-game rewards are key drivers of Route popularity. Players' motivators to choose nature Routes over urban Routes were limited to outside-the-game factors such as scenery, and overall in our experiment the urban Routes turned out to be more popular.

We consider fairness in dimensionality reduction. Nonlinear dimensionality reduction yields low dimensional representations that let users visualize and explore high-dimensional data. However, traditional dimensionality reduction may yield biased visualizations overemphasizing relationships of societal phenomena to sensitive attributes or protected groups. We introduce a framework of fair neighbor embedding, the Fair Neighbor Retrieval Visualizer, which formulates fair nonlinear dimensionality reduction as an information retrieval task whose performance and fairness are quantified by information retrieval criteria. The method optimizes low-dimensional embeddings that preserve high-dimensional data neighborhoods without yielding biased association of such neighborhoods to protected groups. In experiments the method yields fair visualizations outperforming previous methods.

In graph data, each node often serves multiple functionalities. However, most graph embedding models assume that each node can only possess one representation. We address this issue by proposing a nonparametric graph embedding model. The model allows each node to learn multiple representations where they are needed to represent the complexity of random walks in the graph. It extends the Exponential family graph embedding model with two nonparametric prior settings, the Dirichlet process and the uniform process. The model combines the ability of Exponential family graph embedding to take the number of occurrences of context nodes into account with nonparametric priors giving it the flexibility to learn more than one latent representation for each node. The learned embeddings outperform other state of the art approaches in link prediction and node classification tasks.

Abstract We present a search system for grammatically analyzed corpora of Finnish parliamentary records and interviews with former parliamentarians, annotated with metadata of talk structure and involved parliamentarians, and discuss their use through carefully chosen digital humanities case studies. We first introduce the construction, contents, and principles of use of the corpora. Then we discuss the application of the search system and the corpora to study how politicians talk about power, how ideological terms are used in political speech, and how to identify narratives in the data. All case studies stem from questions in the humanities and the social sciences, but rely on the grammatically parsed corpora in both identifying and quantifying passages of interest. Finally, the paper discusses the role of natural language processing methods for questions in the (digital) humanities. It makes the claim that a digital humanities inquiry of parliamentary speech and interviews with politicians cannot only rely on computational humanities modeling, but needs to accommodate a range of perspectives starting with simple searches, quantitative exploration, and ending with modeling. Furthermore, the digital humanities need a more thorough discussion about how the utilization of tools from information science and technologies alter the research questions posed in the humanities.

Modern text data is increasingly gathered in situations where it is paired with a high-dimensional collection of covariates: then both the text, the covariates, and their relationships are of interest to analyze. Despite the growing amount of such data, current topic models are unable to take into account large amounts of covariates successfully: they fail to model structure among covariates and distort findings of both text and covariates. This paper presents a solution: a novel factor-topic model that enables researchers to analyze latent structure in both text and sophisticated document-level covariates collectively. The key innovation is that besides learning the underlying topical structure, the model also learns the underlying factorial structure from the covariates and the interactions between the two structures. A set of tailored variational inference algorithms for efficient computation are provided. Experiments on three different datasets show the model outperforms comparable topic models in the ability to predict held-out document content. Two case studies focusing on Finnish parliamentary election candidates and game players on Steam demonstrate the model discovers semantically meaningful topics, factors, and their interactions. The model both outperforms state-of-the-art models in predictive accuracy and offers new factor-topic insights beyond other topic models.

Recent studies on gaming wearables show that wearables can contribute to the gaming experience by bolstering performativity, facilitating social interaction, and accommodating distinct interaction modalities. Still, these studies focused on contexts such as role-playing, casual, or festival games. Stakeholder-oriented research that explores the integration of wearables for mainstream gaming platforms such as game consoles is scarce. To fill this gap, we have conducted an exploratory study through 6 participatory design workshops focusing on different aspects of wearables with 33 participants from different stakeholders. As a result, we have created fifteen design themes and three gaming wearable concepts that led to seven actionable design implications which can be adopted by designers and researchers for designing gaming wearables.

Postmortems and Reddit Ask Me Anything (AMA) threads represent communications of game developers through two different channels about their game development experiences, culture, processes, and practices. We carry out a quantitative text mining based comprehensive analysis of online available postmortems and AMA threads from game developers over multiple years. We find and analyze underlying topics from the postmortems and AMAs as well as their variation among the data sources and over time. The analysis is done based on structural topic modeling, a probabilistic modeling technique for text mining. The extracted topics reveal differing and common interests as well as their evolution of prevalence over time in the two text sources. We have found that postmortems put more emphasis on detail-oriented development aspects as well as technically-oriented game design problems whereas AMAs feature a wider variety of discussion topics that are related to a more general game development process, game-play and game-play experience related game design. The prevalences of the topics also evolve differently over time in postmortems versus AMAs.

This paper identifies 7 key social features which appear in the marketing and promotional material of hybrid board games. The features are identified by exploring sources such as game websites and game boxes of 13 hybrid board game products. The material is analyzed in order to determine how social features related to hybrid game features are presented. As a result of the analysis, it became apparent that there are certain key social features which are presented as being important to players. The knowledge generated in this work acts as a view to how the industry sees hybridity in games as a tool for supporting social interaction, and how the industry wants to message it to consumers when they explore promotional material. The identified key social features can also be used as design knowledge for developing new games, as they give insight into popular social features in hybrid board games.

The meaning of what hybrid games are is often fixed to the context in which the term is used. For example, hybrid games have often been defined in relation to recent developments in technology. This creates issues in its usage and limitations in thinking. This paper argues that hybrid games should be understood through conceptual metaphors. Hybridity is the blending of different cognitive domains that are not usually associated together. Hybrid games usually blend domains related to games, for example digital and board games, but can blend also other domains. Through this type of thinking, designers can be more open to exploring how their games can be experienced.

In this article we present findings from a design experiment of MurMur Moderators, talking playful seats facilitating playful atmosphere and creativity at office environments. The article describes the design and technological composition of our two prototypes, and our experiences exposing the concept to audiences at science fairs and an office environment. This research has served as an exploratory design study, directing our focus to the seats as primary and secondary play objects with a distinct narrative. Our goal with the initial exposure was to first investigate preliminary audience reactions for the high level concept and how people interact with the prototype. This was then supplemented by testing the concept in an office environment. The data we have collected gives us insight on the seats as primary and secondary play objects and how users touch, discover and socialize.