v Russell, Emma; Woods, Stephen A
Computers in human behavior,
02/2020, Volume:
103
Journal Article
Peer reviewed
Email is a ubiquitous and work-critical tool for many people at work today. Research suggests that people engage a range of actions to deal with work-email, with the same email action (e.g. turning ...off email) facilitating some goals (e.g. for well-being) but hindering others (e.g. for being helpful). Using mixed-methods across two studies with knowledge workers who use work-email, we examined whether individual differences in personality can explain why there is a goal paradox of work-email actions. The theory of purposeful work behavior (TPWB) informs our approach. In Study 1, semi-structured interviews (N = 28) uncovered (using thematic analysis) a comprehensive list of 72 work-email actions that differently impact goals related to Work, Well-being, Control and Concern. Study 2 then addressed whether personality traits could predict work-email activity directed towards these four goals. A multi-level survey (N = 341; n = 5575) of work-email activity was analyzed using cross-level hierarchical linear modelling. We found that action-goal relationships in dealing with work-email, could be predicted by people's trait-relevant goal striving. This advances understanding of why work-email actions can be both beneficial and problematic for people. Use of habitual actions also interacted with personality to strengthen action-goal relationships, except for those with low Emotional Stability. Findings are discussed in terms of their implications for theory, policy and practice.
•A LinearSVM model was able to extract e-mail sentiment with a mean AUC of 0.896.•The model could also predict sentiment for e-mail responses with a mean AUC of 0.805.•The results suggests ...possibilities for improved customer support mangement processes.
Customer support is important to corporate operations, which involves dealing with disgruntled customer and content customers that can have different requirements. As such, it is important to quickly extract the sentiment of support errands. In this study we investigate sentiment analysis in customer support for a large Swedish Telecom corporation. The data set consists of 168,010 e-mails divided into 69,900 conversation threads without any sentiment information available. Therefore, VADER sentiment is used together with a Swedish sentiment lexicon in order to provide initial labeling of the e-mails. The e-mail content and sentiment labels are then used to train two Support Vector Machine models in extracting/classifying the sentiment of e-mails. Further, the ability to predict sentiment of not-yet-seen e-mail responses is investigated. Experimental results show that the LinearSVM model was able to extract sentiment with a mean F1-score of 0.834 and mean AUC of 0.896. Moreover, the LinearSVM algorithm was also able to predict the sentiment of an e-mail one step ahead in the thread (based on the text in the an already sent e-mail) with a mean F1-score of 0.688 and the mean AUC of 0.805. The results indicate a predictable pattern in e-mail conversation that enables predicting the sentiment of a not-yet-seen e-mail. This can be used e.g. to prepare particular actions for customers that are likely to have a negative response. It can also provide feedback on possible sentiment reactions to customer support e-mails.
All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the ...professional and scientific standards expected of a proceedings journal published by IOP Publishing.• Type of peer review: Single-blind peer review by two reviewers• Conference submission management system: web-based system with custom-made, submissions were received and handled via e-mail• Number of submissions received: 225• Number of submissions sent for review: 121• Number of submissions accepted: 102• Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received × 100): 45,3%• Average number of reviews per paper: 2• Total number of reviewers involved: 18• Any additional info on review process: All papers are plagiarism checked by Turnitin software• Contact person for queries:Name: Dr. Ramli (corresponding editor)Affiliation: Department of Physics, Universitas Negeri PadangE-mail: ramli@fmipa.unp.ac.id
Structural diversity in social contagion Ugander, Johan; Backstrom, Lars; Marlow, Cameron ...
Proceedings of the National Academy of Sciences - PNAS,
04/2012, Volume:
109, Issue:
16
Journal Article
Peer reviewed
Open access
The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as ...fads, political opinions, the adoption of new technologies, and financial decisions. Traditional models of social contagion have been based on physical analogies with biological contagion, in which the probability that an individual is affected by the contagion grows monotonically with the size of his or her "contact neighborhood"—the number of affected individuals with whom he or she is in contact. Whereas this contact neighborhood hypothesis has formed the underpinning of essentially all current models, it has been challenging to evaluate it due to the difficulty in obtaining detailed data on individual network neighborhoods during the course of a large-scale contagion process. Here we study this question by analyzing the growth of Facebook, a rare example of a social process with genuinely global adoption. We find that the probability of contagion is tightly controlled by the number of connected components in an individual's contact neighborhood, rather than by the actual size of the neighborhood. Surprisingly, once this "structural diversity" is controlled for, the size of the contact neighborhood is in fact generally a negative predictor of contagion. More broadly, our analysis shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.
All papers published in this volume of IOP Conference Series: Materials Science and Engineering have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert ...referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind • Conference submission management system: Email submission • Number of submissions received: 128 • Number of submissions sent for review: 84 • Number of submissions accepted: 48 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 37.5 • Average number of reviews per paper: 2 (Internal screening + Technical review) • Total number of reviewers involved: 17 • Contact person for queries (please include: name, affiliation, institutional email address) Dr. NOR AZLAN OTHMAN Universiti Teknologi Mara Penang, Malaysia azlan253@uitm.edu.my
All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected ...of a proceedings journal published by IOP Publishing.• Type of peer review: Single Anonymous• Conference submission management system: Submissions were helded via email (ebw2021@mail.ru)• Number of submissions received: 39• Number of submissions sent for review: 39• Number of submissions accepted: 11• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 28.2• Average number of reviews per paper: 2• Total number of reviewers involved: 13• Contact person for queries:Name: Egor TerentyevEmail: terentyevyv@mpei.ruAffiliation: National Research University “MPEI”
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users' preferences. Although numerous efforts ...have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.
Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in ...aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects. Instead of directly regressing the four vertices, we glide the vertex of the horizontal bounding box on each corresponding side to accurately describe a multi-oriented object. Specifically, We regress four length ratios characterizing the relative gliding offset on each corresponding side. This may facilitate the offset learning and avoid the confusion issue of sequential label points for oriented objects. To further remedy the confusion issue for nearly horizontal objects, we also introduce an obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object. We add these five extra target variables to the regression head of faster R-CNN, which requires ignorable extra computation time. Extensive experimental results demonstrate that without bells and whistles, the proposed method achieves superior performances on multiple multi-oriented object detection benchmarks including object detection in aerial images, scene text detection, pedestrian detection in fisheye images.
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