A recent research trend has emerged to identify developers’ emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.

This paper illustrates our work concerning the development of a layered architecture for deciding the situation-aware behavior of a Smart Home Environment (SHE). In the proposed approach, the surface level is directly embedded in the environment, while deeper levels represent the control software and perform progressively abstract and conceptual activities whose results can be fed back to the outside world (environment, user, supervisor). In particular, the reasoning layer is in charge of interpreting and transforming the data, collected through sensors of the smart environment, into high level knowledge about the situation. On the other hand, the learning layer, based on Inductive Logic Programming, suitably exploits the interaction of the user with the system to refine the user model and improve its future behavior. Finally, we provide the description of a typical scenario in which the proposed architecture might operate, along with a practical example of how the system might work.

Adapting the behavior of a smart environment means to tailor its functioning to both context situation and users’ needs and preferences. In this paper we propose an agent-based approach for controlling the behavior of a Smart Environment that, based on the recognized situation and user goal, selects a suitable workflow for combining services of the environment. We use the metaphor of a butler agent that employs user and context modeling to support proactive adaptation of the interaction with the environment. The interaction is adapted to every specific situation the user is in thanks to a class of agents called Interactor Agents.

We present the preliminary results of an ongoing research aimed at investigating the role of social media in the process of trust building, with particular attention to the case of small-medium enterprises (SME). Our findings show that social media contribute to increase the affective trust more than traditional websites. This result suggests that social media have the potential to enhance the business of SMEs other than large companies, by fostering the affective commitment of customers.

In this paper we propose an agent-based approach for controlling the behavior of a Smart Home Environment that, based on the recognized situation and user goal, selects a suitable workflow for combining services of the environment. To this aim we have developed a butler agent that employs user and context modeling for supporting proactive adaptation of the interaction with the house. The user can interact with the proposed services by accepting, declining or changing them. Such a feedback is exploited by the learning component of the butler to refine the user model and improve its future behavior accordingly. In order to provide a description of how the system might work, a practical example is shown.

Technical Q\&A sites have become essential for software engineers as they constantly seek help from other experts to solve their work problems. Despite their success, many questions remain unresolved, sometimes because the asker does not acknowledge any helpful answer. In these cases, an information seeker can only browse all the answers within a question thread to assess their quality as potential solutions. We approach this time-consuming problem as a binary-classification task where a best-answer prediction model is built to identify the accepted answer among those within a resolved question thread, and the candidate solutions to those questions that have received answers but are still unresolved. In this paper, we report on a study aimed at assessing 26 best-answer prediction models in two steps. First, we study how models perform when predicting best answers in Stack Overflow, the most popular Q\&A site for software engineers. Then, we assess performance in a cross-platform setting where the prediction models are trained on Stack Overflow and tested on other technical Q\&A sites. Our findings show that the choice of the classifier and automatied parameter tuning have a large impact on the prediction of the best answer. We also demonstrate that our approach to the best-answer prediction problem is generalizable across technical Q\&A sites. Finally, we provide practical recommendations to Q\&A platform designers to curate and preserve the crowdsourced knowledge shared through these sites.

n this paper, we investigate the user's reactions to received suggestion by an Embodied Conversational Agent playing the role of artificial therapist in the healthy eating domain. Specifically, we analyse the behaviour of people who voluntarily requested to receive information from the agent, and we compare it with the results of a previous evaluation experiment in which subjects were not properly motivated to interact with the agent because they were selected for evaluating the system. This study is part of an ongoing research aimed at developing an intelligent virtual agent that applies natural argumentation techniques to persuade the users to improve their eating habits.

In this paper we present CoRSAR, a mobile recommender system for the tourism domain in Augmented Reality. It allows the users to explore and visit a city and provides recommendation of Point of Interests (POIs) by combining collaborative filtering and context-awareness. In this paper, besides describing the system, we present the results of a study aiming at evaluating if users were more satisfied with the system recommendations when context features were taken into account. Results show that users provided a better evaluation of the system when the context-aware approach was adopted rather then the simple collaborative filtering one.

Embedding the HCI technology with human preferences and behaviour justifies the attempt of implementing emotional and social intelligence aimed at exceeding the single ability to help the user. In this paper we present an Embodied Conversa-tional Agent’s (ECA’s) architecture and methods useful to interpret the user affec-tive attitude during her dialog with an ECA and behaving 'believably' in its turn. In particular, we present an agent architecture that is general enough to be applied in several application domains and that can employ several ECA’s bodies according to the context requirements.

Trust is a concept that has been widely studied in e-commerce since it represents a key issue in building successful customer-supplier relationships. In this sense, social software represents a powerful channel for establishing a direct communication with customers. As a consequence, companies are now investing in social media for building their social digital brand and strengthening relationships with their customers. In this paper we investigate the role of social media in the process of trust building, with particular attention to the case of small companies. Our findings show that social media contribute to build affective trust more than traditional websites, by fostering the affective commitment of customers.

Trust is a concept that has been widely studied in e-commerce since it represents a key issue in building successful customer-supplier relationships. In this sense, social software represents a powerful channel for establishing a direct communication with customers. As a consequence, companies are now investing in social media for building their social digital brand and strengthening relationships with their customers. In this paper, we present the preliminary results of an ongoing research aimed at investigating the role of social media in the process of trust building, with particular attention to the case of small-medium enterprises. Our findings show that social media contribute to build more affective trust than traditional websites, suggesting that social media have the potential to enhance the business of SMEs other than large companies, by fostering the affective commitment of customers.

Recent research has shown that drivers of success in online question answering encompass presentation quality as well as temporal and social aspects. Yet, we argue that also the emotional style of a technical contribution influences its perceived quality. In this paper, we investigate how Stack Overflow users can increase the chance of getting their answer accepted. We focus on actionable factors that can be acted upon by users when writing an answer and making comments. We found evidence that factors related to information presentation, time and affect all have an impact on the success of answers.

Context: Recently, more and more developer communities are abandoning their legacy support forums, moving onto Stack Overflow. The motivations are diverse, yet they typ-ically include achieving faster response time and larger vis-ibility through the access to a modern and very successful infrastructure. One downside of migration, however, is that the history and the crowdsourced knowledge hosted at pre-vious sites remain separated or even get lost if a community decides to abandon completely the legacy developer forum. Goal: Adding to the body of evidence of existing research on best-answer prediction, here we show that, from a techni-cal perspective, the content from existing developer forums might be automatically migrated to the Stack Overflow, al-though most of forums do not allow to mark a question as resolved, a distinctive feature of modern Q&A sites. Method: We trained a binary classifier with data from Stack Overflow and then tested it with data scraped from Do-cusign, a developer forum that has recently completed the move. Results: Our findings show that best answers can be pre-dicted with a good accuracy, only relying on shallow linguis-tic (text) features, such as answer length and the number of sentences, combined with other features like answer upvotes and age, which can be easily computed in near real-time. Conclusions: Results provide an initial yet positive ev-idence towards the automatic migration of crowdsourced knowledge from legacy forums to modern Q&A sites.

Pedagogical Conversational Agents (PCAs) have the advantage of offering to students not only task-oriented support but also the possibility to interact with the computer media at a social level. This form of intelligence is particularly important when the character is employed in an educational setting. This paper reports our initial results on the recognition of users' social response to a pedagogical agent from the linguistic, acoustic and gestural analysis of the student communicative act.

Ambient Intelligence systems require a natural and personalized experience in interacting with services provided by the environment. In this view, the interaction may happen either in a pervasive way, through a combination of devices embedded in the environment, or using a conversational interface acting as an environment concierge. In the latter case, the interface can be embodied in a conversational agent able to involve users in a human-like conversation and to establish a social relation with them. Developing such an Ambient Conversational System (ACS) requires a model of the user that considers not only the cognitive ingredients of his mental state, but also extra-rational factors such as affect, engagement, attitudes. This paper describes a multimodal framework for recognizing the social attitude of users during the interaction with an embodied agent in a smart environment. In particular, we started from the analysis and annotation of advisory dialogs between humans and then we used the annotated corpus to build a framework for recognizing the social attitude in multimodal dialogs with an ACS. Results of the study show an acceptable performance of the framework in recognizing and monitoring the social attitude during the dialog with an ACS. We also compared results of the analysis of human-human interactions with respect to the human-ACS interaction and, even if the level of initiative of subjects during the dialog was lower in this latter modality, the difference in the average number of social moves was not significant, thus showing that subjects probably were keen to establish a social relation with the conversational agent.

Ambient Intelligence aims at promoting an effective, natural and personalized interaction with the environment services. In order to provide the most appropriate answer to the user requests, an Ambient Intelligence system should model the user by considering not only the cognitive ingredients of his mental state, but also extra-rational factors such as affect, engagement, attitude, and so on. This paper describes a study aimed at building a multimodal framework for recognizing the social response of users during interaction with embodied agents in the context of ambient intelligence. In particular, we describe how we extended a model for recognizing the social attitude in text-based dialogs by adding two additional knowledge sources: speech and gestures. Results of the study show that these additional knowledge sources may help in improving the recognition of the users' attitude during interaction.

Pedagogical Conversational Agents (PCAs) have the advantage of offering to students not only task-oriented support but also the possibility to interact with the computer media at a social level. This form of intelligence is particularly important when the character is employed in an educational setting. This paper reports our initial results on the recognition of users' social response to a pedagogical agent from the linguistic, acoustic and gestural analysis of the student communicative act.

The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines.

Conversational agents have been widely used in pedagogical contexts. They have the advantage of offering to users not only a task-oriented support, but also the possibility to relate with the system at social level. Therefore, besides endowing the conversational agent with knowledge necessary to fulfill pedagogical goals, it is important to provide the agent with social intelligence. To do so the agent should be able to recognize the social attitude of the user during the interaction in order to accommodate the conversational strategy. In this paper we illustrate how we defined and applied a model for recognizing the social attitude of the student in natural interaction with a Pedagogical Conversational Agent (PCA) starting from the linguistic, acoustic and gestural analysis of the communicative act.

In this paper, we present the proposal for a partial replication of a controlled experiment to further assess how knowing personal and expertise information about other team members may enhance initial trust building. Other than increasing confidence into the findings of the original study, we also aim at evaluating whether the provision of personal social media information, can lead to even higher level of trust in virtual teams.

Nowadays, people increasingly seek information and ask for help on Question and Answer (Q&A) sites. The enormous success of Stack Exchange , a constantly growing network of Q&A sites, attests this increasing trend. The success of Q&A mainly depends on the will of their members to provide good quality answers to others’ questions. We investigate the success factors of Q&A that is those factors that foster effective knowledge creation and sharing. In particular, we focus those factors that can be acted upon by contributors when writing a question.

In this contribution, we argue in favor of the importance of the role that interaction plays in supporting effective e-learning processes. Interaction among peers, in fact, either with real or virtual companions, is the fundamental component of the process of building and sharing new knowledge. In particular, we intend to argue in favor of a constructivist approach to e-learning. We support our view by reporting on our experience in the design of a platform for language learning through dramatization. Finally, we present the direction we intend to follow in our future research, towards the definition of an approach for enhancing effective interaction during collaborative learning and knowledge sharing in online learning environments.

In this contribution, we argue in favor of the importance of the role that interaction plays in supporting effective e-learning processes. Interaction among peers, in fact, either with real or virtual companions, is the fundamental component of the process of building and sharing new knowledge. In particular, we intend to argue in favor of a constructivist approach to e-learning. We support our view by reporting on our experience in the design of a platform for language learning through dramatization. Finally, we present the direction we intend to follow in our future research, towards the definition of an approach for enhancing effective interaction during collaborative learning and knowledge sharing in online learning environments.

A recent research trend has emerged to study the role of affect in in the social programmer ecosystem, by applying sentiment analysis to the content available in sites such as GitHub and Stack Overflow. In this paper, we aim at assessing the suitability of a state-of-the-art sentiment analysis tool, already applied in social computing, for detecting affective expressions in Stack Overflow. We also aim at verifying the construct validity of choosing sentiment polarity and strength as an appropriate way to operationalize affective states in empirical studies on Stack Overflow. Finally, we underline the need to overcome the limitations induced by domain-dependent use of lexicon that may produce unreliable results.

We present a qualitative analysis of the lexicon of Dialogue Acts: we explore the relationship between the communicative goal of an utterance and its affective lexicon as well as the salience of specific word classes for each speech act. Thought not constituting any deep understanding of the dialogue, automatic dialogue act labeling is a task that may be relevant for a wide range of applications in both human-computer and human-human interaction. The experiments described in this paper fit in the scope of a research study whose long-term goal is to build an unsupervised classifier that simply exploits the lexical semantics of utterances to automatically annotate dialogues with the proper speech acts.

Trust represents a key issue in building successful customer-supplier relationships. In this sense, social software represents a powerful means for fostering trust by establishing a direct, more personal communication channel with customers. Therefore, companies are now investing in so-cial media for building their social digital brand and strengthening relationships with their cus-tomers. In this paper, we presented two experiments by means of which we investigated the role of traditional websites and social media in trust building along the cognitive and affective di-mensions. We hypothesize that traditional websites (content-oriented) and social media (interac-tion-oriented) may have a different effect on trust building in customer-supplier relationships, based on the first impression provided to potential customers. Although additional research is still needed, our findings add to the existing body of evidence that both cognitive and affective trust can be successfully fostered through online presence. Specifically, social media provides companies with tools to communicate benevolence to potential customer and, therefore, foster the affective commitment of customers. Traditional websites, instead, are more appropriate for communicating the competence and reliability of a company, by fostering trust building along the cognitive dimension. The results of our studies provide implications for researchers and practi-tioners, by highlighting the importance of combining the two media for effectively building a trustworthy online company image.

The problem of implementing socially intelligent agents has been widely investigated in the field of both Embodied Conversational Agents (ECAs) and Social Robots that have the advantage of offering to people the possibility to relate with computer media at a social level. We focus our study on the recognition of the social response of users to embodied agents in the context of ambient intelligence. In this paper we describe how we extended a model for recognizing the social attitude in natural conversation from text by adding two additional knowledge sources: speech and gestures.

Today, people increasingly try to solve domain-specific problems through interaction on online Question and Answer (Q&A) sites, such as Stack Overflow. The growing success of the Stack Overflow community largely depends on the will of their members to answer others' questions. Recent research has shown that the factors that push members of online communities encompass both social and technical aspects. Yet, we argue that also the emotional style of a technical question does influence the probability of promptly obtaining a satisfying answer. In this paper, we describe the design of an empirical study aimed to investigate the role of affective lexicon on the questions posted in Stack Overflow.

In this contribution, we argue in favor of the importance of the role that interaction plays in supporting effective e-learning processes. Interaction among peers, in fact, either with real or virtual companions, is the fundamental component of the process of building and sharing new knowledge. In particular, we intend to argue in favor of a constructivist approach to e-learning. We support our view by reporting on our experience in the design of a platform for language learning through dramatization. Finally, we present the direction we intend to follow in our future research, towards the definition of an approach for enhancing effective interaction during collaborative learning and knowledge sharing in online learning environments.

In this contribution, we describe our preliminary statement towards the long-term goal of creating an intelligent agent for monitoring interaction in the communication environments of the communities of practice. The assumption underlying our research work is that embedding some form of artificial intelligence in these environments enhances effective interaction during collaborative learning and knowledge sharing. We start by providing a description of the theoretical conceptual framework, illustrate the state of the art in the domain and conclude by sketching the guidelines for a method we intend to implement and test in our future work on real data collected through a web forum developed in our previous research

The technology introduction and diffusion operated a transformation in the job market determining the creation of new professional expertise and the development of technologies to support the collaborative building of knowledge. In this paper we analyse the role of the communities of practice and highlight the importance of the intelligent analysis of computer-mediated interactions for the creation of new professional knowledge.

Ambient Intelligence solutions may provide a great opportunity, for elder people, to live longer at home. When assistance and care are delegated to the intelligence embedded in the environment, besides considering task-oriented response to the user needs, it is necessary to take into account the establishment of social relations. To this aim, it becomes crucial to model both the rational and the affective components of the user state of mind. In this chapter we will mainly focus on the problem of modeling the cognitive and affective variables involved in the definition of a user model suitable for this domain. After provid-ing an overlook of the state of the art, we report about our experience in designing NICA (as the name of the project Natural Interaction with a Caring Agent), a social agent acting as a virtual caregiver able to assist elderly people in a smart environment for taking care of both the physical and mental state of the users.