eCitizen2.0. The ordinary citizen as a supplier of public-sector information

-The growth of new technologies and ways of using them has led to rapid changes in the public-sector information and services situation. Today, 17 percent of Internet users regularly download public-sector information from user-generated fora on the Internet. This report has studied these changes with the aim of developing new ideas and perspectives for the eGov sector, on which citizens (eCitizens2.0) are also suppliers or services and producers of public-sector information

eBorger2.0 Den alminnerlige borger som leverandør av offentlig informasjon?

-Fremveksten av nye teknologier og bruksmåter har på kort tid endret betingelsen til offentlig informasjon og tjenester. I dag henter 17 prosent av nettbefolkningen offentlig informasjon i brukerskapte fora på Internett regelmessig. Denne rapporten har studert disse endringene for å utvikle nye idéer ig perspektiver for eForvaltningen der borgerne (eBorger2.0) også er tjenesteleverandører og produsenter av offentlig informasjon

Hjelp – mobilzombiene kommer!

Vi er besatt av smarttelefonen vår. Hvorfor ble det sånn? Og hva skjer fremover?Hjelp – mobilzombiene kommer!submittedVersio

A Longitudinal Study of Self-Disclosure in Human-Chatbot Relationships

Self-disclosure in human–chatbot relationship (HCR) formation has attracted substantial interest. According to social penetration theory, self-disclosure varies in breadth and depth and is influenced by perceived rewards and costs. While previous research has addressed self-disclosure in the context of chatbots, little is known about users' qualitative understanding of such self-disclosure and how self-disclosure develops in HCR. To close this gap, we conducted a 12-week qualitative longitudinal study (n = 28) with biweekly questionnaire-based check-ins. Our results show that while HCRs display substantial conversational breadth, with topics spanning from emotional issues to everyday activities, this may be reduced as the HCR matures. Our results also motivate a nuanced understanding of conversational depth, where even conversations about daily activities or play and fantasy can be experienced as personal or intimate. Finally, our analysis demonstrates that conversational depth can develop in at least four ways, influenced by perceived rewards and costs. Theoretical and practical implications are discussed.publishedVersio

Compartmentalization of TNF and IL-6 in meningitis and septic shock

We examined the compartmentalization of bioactive tumour necrosis factor (TNF) and interleukin 6 (IL-6) to the subarachnoid space and systemic circulation in patients with meningococcal meningitis and septic shock/bacteraemia. In patients with meningitis, median levels of TNF in 31 paired samples of cerebrospinal fluid (CSF) and serum were respectively 783 pg/ml and below detection limit (p < 0.001) and median levels of IL-6 were 150 ng/ml and 0.3 ng/ml (p < 0.0001). In patients with septic shock without meningitis, median levels in paired samples of CSF and serum were respectively below detection limit and 65 pg/ml (not significant, (ns)) (TNF, eleven patients) and 1.3 ng/ml–3 ng/ml (ns) (IL-6, nine patients). The data show that TNF and IL-6 are localized to the subarachnoid space in patients with meningitis although the blood–brain barrier is penetrable to serum proteins. On the other hand, patients with septic shock tend to have cytokines in both serum and CSF

My Chatbot Companion – a Study of Human-Chatbot Relationships

There has been a recent surge of interest in social chatbots, and human–chatbot relationships (HCRs) are becoming more prevalent, but little knowledge exists on how HCRs develop and may impact the broader social context of the users. Guided by Social Penetration Theory, we interviewed 18 participants, all of whom had developed a friendship with a social chatbot named Replika, to understand the HCR development process. We find that at the outset, HCRs typically have a superficial character motivated by the users' curiosity. The evolving HCRs are characterised by substantial affective exploration and engagement as the users' trust and engagement in self-disclosure increase. As the relationship evolves to a stable state, the frequency of interactions may decrease, but the relationship can still be seen as having substantial affective and social value. The relationship with the social chatbot was found to be rewarding to its users, positively impacting the participants' perceived wellbeing. Key chatbot characteristics facilitating relationship development included the chatbot being seen as accepting, understanding and non-judgmental. The perceived impact on the users' broader social context was mixed, and a sense of stigma associated with HCRs was reported. We propose an initial model representing the HCR development identified in this study and suggest avenues for future research.publishedVersio

A longitudinal study of human–chatbot relationships

Social chatbots have become more advanced, paving the way for human–chatbot relationships (HCRs). Although this phenomenon has already received some research attention, the results have been contradictory, and there is uncertainty regarding how to understand HCR formation. To provide the needed knowledge on this phenomenon, we conducted a qualitative longitudinal study. We interviewed 25 participants over a 12-week period to understand how their HCRs formed with the popular chatbot Replika. We found that the HCRs formed gradually and mostly in line with the assumptions of Social Penetration Theory. Our findings indicate the need to acknowledge substantial variation and nuance in the HCR formation process, plus variation in the onset of self-disclosure and in the subsequent relationship formation. The results show that important drivers pushing the relationship toward attachment and perceived closeness appear to be Replika's ability to participate in a variety of interactions, as well as to support more deep-felt human needs related to social contact and self-reflection. In contrast, unpredictable events and technical difficulties could hinder relationship formation and lead to termination. Finally, we discuss the appropriateness of using a theoretical framework developed for human–human relationships when investigating HCRs, and we suggest directions for future research.publishedVersio

Deep learning for prediction of depressive symptoms in a large textual dataset

Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.publishedVersio

Chatbots for social good

Chatbots are emerging as an increasingly important area for the HCI community, as they provide a novel means for users to interact with service providers. Due to their conversational character, chatbots are potentially effective tools for engaging with customers, and are often developed with commercial interests at the core. However, chatbots also represent opportunities for positive social impact. Chatbots can make needed services more accessible, available, and affordable. They can strengthen users' autonomy, competence, and (possibly counter-intuitively) social relatedness. In this SIG we address the possible social benefits of chatbots and conversational user interfaces. We will bring together the existing, but disparate, community of researchers and practitioners within the CHI community and broader fields who have an interest in chatbots. We aim to discuss the potential for chatbots to move beyond their assumed role as channels for commercial service providers, explore how they may be used for social good, and how the HCI community may contribute to realize this.acceptedVersio

Facebook is no "great equalizer": A big data approach to gender differences in civic engagement across countries

Facebook is expected to facilitate more equal participation in civic engagement across genders and countries. With the use of a big data tool (Wisdom), we explored gender disparities in various Facebook liking practices concerning expressions of civic engagement among 21,706,806 Facebook users in ten countries across Asia, Africa, the Americas, and Europe. We observed distinct patterns with regard to civic and political expressions on Facebook, with males drawn more toward politically and information-oriented liking practices as compared to females. Moreover, females (aged 13–28 years) in Europe and the Americas are more likely than males to support humanitarian aid and environmental issues on Facebook. This latter finding was not evident in Asia and Africa, where males are more active in liking all forms of civic expressions on Facebook. In conclusion, this study shows that the gender differences in civic engagement that exist offline to a large degree are replicated and reinforced on Facebook.submittedVersio