Unpacking the social media–driven innovation capability: How consumer conversations turn into organizational innovations

Given the scale and scope of consumer conversations on social media, it is both possible and challenging for organizations to identify valuable ideas from those conversations that could lead to successful innovations. We examine how a large retailer developed a new capability to leverage public social media conversations for innovation through a process of filtering, assessing, converting, and deploying. By inductively developing a process model of how the retailer managed to convert consumer conversations on social media into organizational innovation, our study sheds light on the microfoundations of this important capability.     

Animating synthetic dyadic conversations with variations based on context and agent attributes

Conversations between two people are ubiquitous in many inhabited contexts. The kinds of conversations that occur depend on several factors, including the time, the location of the participating agents, the spatial relationship between the agents, and the type of conversation in which they are engaged. The statistical distribution of dyadic conversations among a population of agents will therefore depend on these factors. In addition, the conversation types, flow, and duration will depend on agent attributes such as interpersonal relationships, emotional state, personal priorities, and socio‐cultural proxemics. We present a framework for distributing conversations among virtual embodied agents in a real‐time simulation. To avoid generating actual language dialogues, we express variations in the conversational flow by using behavior trees implementing a set of conversation archetypes. The flow of these behavior trees depends in part on the agents' attributes and progresses based on parametrically estimated transitional probabilities. With the participating agents' state, a ‘smart event’ model steers the interchange to different possible outcomes as it executes. Example behavior trees are developed for two conversation archetypes: buyer–seller negotiations and simple asking–answering; the model can be readily extended to others. Because the conversation archetype is known to participating agents, they can animate their gestures appropriate to their conversational state. The resulting animated conversations demonstrate reasonable variety and variability within the environmental context. Copyright © 2012 John Wiley & Sons, Ltd.     

Online pairing of VoIP conversations

This paper answers the following question; given a multiplicity of evolving 1-way conversations, can a machine or an algorithm discern the conversational pairs in an online fashion, without understanding the content of the communications? Our analysis indicates that this is possible, and can be achieved just by exploiting the temporal dynamics inherent in a conversation. We also show that our findings are applicable for anonymous and encrypted conversations over VoIP networks. We achieve this by exploiting the aperiodic inter-departure time of VoIP packets, hence trivializing each VoIP stream into a binary time-series, indicating the voice activity of each stream. We propose effective techniques that progressively pair conversing parties with high accuracy and in a limited amount of time. Our findings are verified empirically on a dataset consisting of 1,000 conversations. We obtain very high pairing accuracy that reaches 97% after 5 min of voice conversations. Using a modeling approach we also demonstrate analytically that our result can be extended over an unlimited number of conversations.     

Simulating conversations: The communion game

In their enthusiasm for programming, computational linguists have tended to lose sight of what humansdo. They have conceived of conversations as independent of sound and the bodies that produce it. Thus, implicit in their simulations is the assumption that the text is the essence of talk. In fact, unlike electronic mail, conversations are acoustic events. During everyday talk, human understanding depends both on the words spoken and on fine interpersonal vocal coordination. When utterances are analysed into sequences of word-based forms, however, these prosodic aspects of language disappear. Therefore, to investigate the possibility that machines might talk, we propose acommunion game that includes this interpersonal patterning. Humans and machines would talk together and, based on recordings of them, a panel would appraise the relevant merit of each machine's simulation by how true to life it sounded. Unlike Turing's imitation game, the communion game overtly focuses attention, not on intelligence, but on language. It is designed to facilitate the development of social groups of adaptive robots that exploit complex acoustic signals in real time. We consider how the development of such machines might be approached.     

新课改下中学历史教学与信息技术整合利弊

随着信息技术的发展进步,使得中学教学资源的进一步丰富,多媒体技术应用于中学课堂已成为普遍现象,甚至有替代传统教学手段成为主流的趋势。运用信息技术的利弊被一线教师及教育者广泛讨论,利大于弊,但需要老师结合具体教学内容,这一观点也得到大多数人的认可。本文以中学历史教学为例,简析运用中学信息技术的利弊。     

On the refactoring of activity labels in business process models

Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects.     

Statistics-based Motion Synthesis for Social Conversations

Plausible conversations among characters are required to generate the ambiance of social settings such as a restaurant, hotel lobby, or cocktail party. In this paper, we propose a motion synthesis technique that can rapidly generate animated motion for characters engaged in two-party conversations. Our system synthesizes gestures and other body motions for dyadic conversations that synchronize with novel input audio clips. Human conversations feature many different forms of coordination and synchronization. For example, speakers use hand gestures to emphasize important points, and listeners often nod in agreement or acknowledgment. To achieve the desired degree of realism, our method first constructs a motion graph that preserves the statistics of a database of recorded conversations performed by a pair of actors. This graph is then used to search for a motion sequence that respects three forms of audio-motion coordination in human conversations: coordination to phonemic clause, listener response, and partner's hesitation pause. We assess the quality of the generated animations through a user study that compares them to the originally recorded motion and evaluate the effects of each type of audio-motion coordination via ablation studies.     

Contextualized mobile recommendation service based on interactive social network discovered from mobile users

Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each person’s activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbor’s contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as propositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users.     

Synchronizability of conversations among Web services

We present a framework for analyzing interactions among Web services that communicate with asynchronous messages. We model the interactions among the peers participating in a composite Web service as conversations, the global sequences of messages exchanged among the peers. This naturally leads to the following model checking problem: Given an LTL property and a composite Web service, do the conversations generated by the composite Web service satisfy the property? We show that asynchronous messaging leads to state space explosion for bounded message queues and undecidability of the model checking problem for unbounded message queues. We propose a technique called synchronizability analysis to tackle this problem. If a composite Web service is synchronizable, its conversation set remains the same when asynchronous communication is replaced with synchronous communication. We give a set of sufficient conditions that guarantee synchronizability and that can be checked statically. Based on our synchronizability results, we show that a large class of composite Web services with unbounded message queues can be verified completely using a finite state model checker such as SPIN. We also show that synchronizability analysis can be used to check the reliability of top-down conversation specifications and we contrast the conversation model with the Message Sequence Charts. We integrated synchronizability analysis to a tool we developed for analyzing composite Web services.     

Collaborative conversational interfaces

This paper proposes a method of designing human-computer speech interfaces based on principles of human conversation. It argues that conversation is the primary mode of language use and that it is fundamentally collaborative. Speech interfaces should therefore be designed to recreate the collaborative nature of natural conversations. The paper presents five strategies for designingcollaborative conversational interfaces, and it describes the principles of human-language use that underly these strategies. The paper also argues that collaborative conversational interfaces have a crucial advantage over other kinds of interfaces in that they are readily adaptive to different levels of experience and styles of use. The paper gives examples of collaborative conversational interfaces that we have developed, and discusses the ways in which these interfaces have been made adaptive.     

Automatic nonverbal analysis of social interaction in small groups: A review

An increasing awareness of the scientific and technological value of the automatic understanding of face-to-face social interaction has motivated in the past few years a surge of interest in the devising of computational techniques for conversational analysis. As an alternative to existing linguistic approaches for the automatic analysis of conversations, a relatively recent domain is using findings in social cognition, social psychology, and communication that have established the key role that nonverbal communication plays in the formation, maintenance, and evolution of a number of fundamental social constructs, which emerge from face-to-face interactions in time scales that range from short glimpses all the way to long-term encounters. Small group conversations are a specific case on which much of this work has been conducted. This paper reviews the existing literature on automatic analysis of small group conversations using nonverbal communication, and aims at bridging the current fragmentation of the work in this domain, currently split among half a dozen technical communities. The review is organized around the main themes studied in the literature and discusses, in a comparative fashion, about 100 works addressing problems related to the computational modeling of interaction management, internal states, personality traits, and social relationships in small group conversations, along with pointers to the relevant literature in social science. Some of the many open challenges and opportunities in this domain are also discussed.     

SocoTraveler: Travel-package recommendations leveraging social influence of different relationship types

The immense amount of data generated and collected on e-commerce platforms provides opportunities and challenges for big data analytics to create business value. E-tourism platforms collect not only users’ travel information but also users’ social connection information and need effective personalized recommendation systems for target marketing. In this paper, we aim to study how different types of social relationships such as colleague, schoolmate, and relative between co-travelers influence a user’s travel behavior and how to use this influence to enhance recommendation quality. To this end, we develop a probabilistic topic model leveraging individual travel history and social influence of co-travelers to capture personal interests and propose a recommendation method to utilize the proposed model. Experiments on a real travel dataset show that the proposed approach significantly outperforms benchmarks. The result highlights useful findings for travel agencies.     

Constant companions: Instant messaging conversations as sustainable supportive study structures amongst undergraduate peers

Universities are facing severe cuts in funding and a transformation of both the economic model underpinning higher education and the relationship between students, tutors and universities and the traditional forms of support for students’ learning may be eroded. At the same time, mobile communications, instant messaging and social networking are now widespread amongst students and offer possible opportunities for developing new models of support. This paper reports on the use of instant messaging amongst peers, as part of a study of digitally-mediated communication and collaborative activities, crossing formal and informal boundaries, amongst campus-based undergraduate students, at a large university in the UK. It is argued that instant messaging conversations offers a means of sustainable peer support for students by demonstrating how they emerge from everyday practices, drawing on existing relationships and shared histories and redrawing the boundaries between formal and informal settings and practices. The importance of time is highlighted, showing how longitudinal, dialogic conversations were important for both practical and empathetic support. The potential of the instant messaging conversations in supporting the co-creation of artefacts, meaning making, motivation and affective support are also demonstrated, drawing on detailed examples of authentic conversations. However, such peer support practices remain largely invisible and therefore need acknowledging, fostering and encouraging, working alongside students to understand and develop these ideas so that peer support in universities can build on the existing practices of students themselves.     

The impact of communication technologies on life and relationship satisfaction

Previous studies have shown a relationship between the use of communications technology and well-being, particularly mediated through its effect on personal relationships. However, there is some debate over whether this effect is positive or negative. The present study explored this issue further, examining whether the effect varies depending on the type of communications technology, and the nature of the personal relationship. An online survey was conducted with 3421 participants in three countries (Australia, UK and US). It examined the use of ten communication methods, overall satisfaction with life and satisfaction with four different kinds of relationships (close and extended family, and close and distant friends).Results indicate that richer communication methods, which include non-verbal cues, were positively associated with both overall satisfaction with life and satisfaction with relationships. These methods included face-to-face communication, and phone and video calls. Conversely, more restricted methods, such as text messaging and instant messaging, were negatively associated with both variables. Social networking was negatively associated with overall satisfaction, but not with satisfaction with relationships. The strength of the association between a communications method and satisfaction with a relationship varied depending on the type of relationship, but whether it was positive or negative did not change.     

Sequence Package Analysis: a new natural language understanding method for improving human response in critical systems

This paper will demonstrate how Sequence Package Analysis, as a new natural language understanding method that is built on a set of parsing structures that consist of context-free grammatical units and related prosodic features for identifying affective/emotional data found in natural speech and blogs, may better accommodate the goals of crisis management and rapid decision making in critical systems. Following an in depth discussion of the genesis and development of this method for the design of voice-user interfaces and audio mining programs, debuted in an earlier issue of IJST, this paper will attempt to show how Sequence Package Analysis can improve human response in monitoring recorded conversations of terror suspects and the recordings of help-line desks. In both instances, effective human intervention may help avert a crisis and resulting liability. The paper’s limited focus on these two respective domains does not, however, limit the applicability of Sequence Package Analysis to other critical systems, inasmuch as the parsing structures explained below are generic enough to be applied to other critical systems, such as ambulance control, aircraft or nuclear power stations, or 911 calls for help. Given that critical systems require effective human response from decision makers, natural language data must be accorded the same kind of scientific scrutiny given to graphic design and other features of human-computer interaction.     

A collaborative behavior-based approach for handling ambiguity, uncertainty, and vagueness in robot natural language interfaces

Service robots are used by ordinary people in their houses and offices. For such users, a desirable way to communicate with robots is through natural language interfaces. So far, techniques for developing robot natural language interfaces are far from mature. A major challenge is handling ambiguity, uncertainty, and vagueness in parsing, object resolution, and vague natural language words. In this research, we develop a new approach called the collaborative behavior-based approach, in which behaviors of robots and behaviors of human users, as well as the changes of object states caused by the behaviors, are taken into consideration integratedly in processing natural language user instructions. In this paper, we analyze the special features of a human–robot interface that may affect language understanding, describe our approach that is designed based on the features, and present the implementation and some experimental results.     

Four common multicommunicating misconceptions

Multicommunicating (MC) represents a form of multitasking in which employees such as IS analysts and managers engage in multiple conversations at the same time (e.g., by sending texts while on a telephone call). MC can occur either during group meetings or during one-on-one conversations: the present paper focuses on the latter, termed dyadic MC. MC is increasingly prevalent in the workplace and is often useful in today’s business world, for example by making it possible to respond in a timely manner to urgent communications. Nonetheless, the efficacy of MC behaviors can also be questioned as they have been found to negatively affect performance and workplace relationships, as well as causing stress. During our investigations of this phenomenon, we often heard IS practitioners say ‘So what? I do this all the time, it’s no problem!’ which suggests that certain misconceptions regarding MC behaviors may be prevalent. Arising from research findings in multiple disciplines, we examine four such practitioner beliefs regarding MC behaviors: MC makes employees more accessible, it enhances productivity, it is required in most jobs, and rudeness is not an issue when MC. Further, we suggest recommendations to IS employees and managers so that they can better manage MC.     

Graph Embedding Based API Graph Search and Recommendation

Searching application programming interfaces (APIs) is very important for developers to reuse software projects. Existing natural language based API search mainly faces the following challenges. 1) More accurate results are required as software projects evolve to be more heterogeneous and complex. 2) The semantic relationships between APIs (e.g., inheritances between classes, and invocations between methods) need to be illustrated so that developers can better understand their usage scenarios. To deal with these issues, we propose GeAPI, a novel graph embedding based approach for API graph search and recommendation in this paper. First, we build a software project's API graph automatically from its source code and represent each API using graph embedding methods. Second, we search the API graph with a question in natural language, and return the corresponding subgraph that is composed of relevant code elements and their associated relationships, as the best answer of the question. In experiments, we select three well-known open source projects, JodaTime, Apache Lucene and POI, as examples to perform API search tasks. The experimental results show that our approach GeAPI improves F1-score by 10% compared with the existing shortest path based API search approach, while reduces the average response time about 60 times.