Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

As social robots see increasing deployment within the general public, improving the interaction with those robots is essential. Spoken language offers an intuitive interface for the human–robot interaction (HRI), with dialogue management (DM) being a key component in those interactive systems. Yet, to overcome current challenges and manage smooth, informative, and engaging interaction, a more structural approach to combining HRI and DM is needed. In this systematic review, we analyze the current use of DM in HRI and focus on the type of dialogue manager used, its capabilities, evaluation methods, and the challenges specific to DM in HRI. We identify the challenges and current scientific frontier related to the DM approach, interaction domain, robot appearance, physical situatedness, and multimodality.

Background: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. At the moment, such monitoring is done by means of regular sentiment analysis with a poor performance on detecting negative stance towards vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms.Results: We found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, considerably outperforming the currently used sentiment analysis that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. We also show that the recall of our system could be optimized to 0.60 at little loss of precision.Conclusion: The outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Nonetheless, the model showed sufficient recall on identifying negative tweets so as to reduce the manual effort of reviewing messages. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions.

Explicit references on Twitter to future events can be leveraged to feed a fully automatic monitoring system of real-world events. We describe a system that extracts open-domain future events from the Twitter stream. It detects future time expressions and entity mentions in tweets, clusters tweets together that overlap in these mentions above certain thresholds, and summarizes these clusters into event descriptions that can be presented to users of the system. Terms for the event description are selected in an unsupervised fashion.¹We evaluated the system on a month of Dutch tweets, by showing the top-250 ranked events found in this month to human annotators. Eighty per cent of the candidate events were indeed assessed as being an event by at least three out of four human annotators, while all four annotators regarded sixty-three per cent as a real event. An added component to complement event descriptions with additional terms was not assessed better than the original system, due to the occasional addition of redundant terms. Comparing the found events to gold-standard events from maintained calendars on the Web mentioned in at least five tweets, the system yields a recall-at-250 of 0.20 and a recall based on all retrieved events of 0.40.

In a crisis situation, communication is an important asset for safeguarding the reputation of an organization. The communication strategy that is used in a crisis influences the way people perceive the crisis. While extensive research is conducted and clear communication guidelines about crisis communication are provided, current research tends to focus on a single actor in a crisis within a homogeneous stakeholder group. In this article, we analyze whether and how different groups of stakeholders frame a crisis and the extent to which they attribute responsibility for the crisis to actors. The case concerns the use of an illegal lice detergent (fipronil) in eggs in the Netherlands in the summer of 2017. Based on the analysis of Twitter data related to the case using multiple methods (network analysis, a longitudinal analysis and the annotation of a sample of tweets), this study shows that a seemingly simple case in a single sub-arena has different subgroups that use different frames and attribute different responsibilities to different stakeholders. This result implies that a reconsideration of communication strategies during and after a crisis is needed.