In an age where data is a pivotal asset for businesses, the ethical acquisition and use of personal information has become increasingly more significant. Empowering data providers with greater autonomy over their personal data is more important than ever. To address this, we propose a novel negotiation-based information-sharing framework that empowers individuals to actively negotiate the terms of their data sharing, addressing privacy concerns and ethical data usage. The framework enables users to determine what personal information they share and under what conditions, fostering a more balanced and transparent data exchange process. Our system allows data consumer agents to negotiate with their human users and can operate fully automatically, with agents representing data providers negotiating based on elicited preferences and needs. We propose novel preference modeling approaches and a negotiation framework to facilitate the bilateral sharing of information and incentives between data consumers and providers. User experiments demonstrate the efficacy of our negotiation approach and the effectiveness of the proposed preference models. Empirical results validate the benefits of the proposed framework.

When mass collaborations face situations that are not readily resolvable, just like traditional organizations, they need to form task forces to address these complicated problems. Referred to as temporary organizations, small, task-focused, time-bound, agile groups, these task forces exist in organizations and corporations to address situations outside of existing procedures. Temporary organizations can be created in online platforms, like mass collaborations, to serve a similar purpose, as existing policies and procedures cannot address all situations that arise during collaboration. Given that mass collaborations are people-driven, informal, and voluntary, this study explores the impact of the dimensions of social capital on the success of online temporary organizations, exploring its structural, relational, and cognitive aspects. We suggest that participants’ access to social capital and their willingness to exercise it lead to a greater likelihood of success.

When a crowd’s motivations are not triggered, they may not necessarily commit their best efforts, even if they have the knowledge to answer an open call. Drawing on the incentive theory, we introduce a top-down process model for an online crowdsourcing campaign that addresses the crowd’s motivations. This model is in contrast to the traditional bottom-up process model, where the crowd self-selects an open call based on their knowledge. We adopt a longitudinal case study method and examine two online crowdsourcing campaigns that represent both models. The findings suggest that the campaign that follows the top-down model generated high-quality ideas, while the bottom-up case was considered a failure. We further enrich the top-down model by developing a four-stage guidance model that addresses the crowd’s differing motivations in each stage. This research contributes to the crowdsourcing literature and helps better attract the qualifying crowd, thereby leading to greater campaign success likelihood.

People can benefit from disclosing negative emotions or stigmatized facets of their identities, and psychologists have noted that imagery can be an effective medium for expressing difficult emotions. Social network sites like Instagram offer unprecedented opportunity for image-based sharing. In this paper, we investigate sensitive self-disclosures on Instagram and the responses they attract. We use visual and textual qualitative content analysis and statistical methods to analyze self-disclosures, associated comments, and relationships between them. We find that people use Instagram to engage in social exchange and story-telling about difficult experiences. We find considerable evidence of social support, a sense of community, and little aggression or support for harmful or pro-disease behaviors. Finally, we report on factors that influence engagement and the type of comments these disclosures attract. Personal narratives, food and beverage, references to illness, and self-appearance concerns are more likely to attract positive social support. Posts seeking support attract significantly more comments. CAUTION: This paper includes some detailed examples of content about eating disorders and self-injury illnesses.

Scientific theories and models in Earth science typically involve changing variables and their complex interactions, including correlations, causal relations and chains of positive/negative feedback loops.Variables tend to be complex rather than atomic entities and expressed as noun phrases containing multiple modifiers, e.g.oxygen depletion in the upper 500 m of the ocean or timing and magnitude of surface temperature evolution in the Southern Hemisphere in deglacial proxy records.Text mining from Earth science literature is therefore significantly different from biomedical text mining and requires different approaches and methods.Our approach aims at automatically locating and extracting variables and their direction of variation: increasing, decreasing or just changing.Variables are initially extracted by matching tree patterns onto the syntax trees of the source texts.Next, variables are generalised in order to enhance their similarity, facilitating hierarchical search and inference.This generalisation is accomplished by progressive pruning of syntax trees using a set of tree transformation operations.Text mining results are presented as a browsable variable hierarchy which allows users to inspect all mentions of a particular variable type in the text as well as any generalisations or specialisations.The approach is demonstrated on a corpus of 10k abstracts of Nature publications in the field of Marine science.We discuss experiences with this early prototype and outline a number of possible improvements and directions for future research.

On-demand mobile workforce applications match physical world tasks and willing workers. These systems offer to help conserve resources, streamline courses of action, and increase market efficiency for micro- and mid-level tasks, from verifying the existence of a pothole to walking a neighbor's dog. This study reports on the motivations and experiences of individuals who regularly complete physical world tasks posted in on-demand mobile workforce marketplaces. Data collection included semi-structured interviews with members (workers) of two different services. The analysis revealed the main drivers for participating in an on-demand mobile workforce, including desires for monetary compensation and control over schedules and task selection. We also reveal main reasons for task selection, which involve situational factors, convenient physical locations, and task requester profile information. Finally, we discuss the key characteristics of the most worthwhile tasks and offer implications for novel crowdsourcing systems for physical world tasks.