20,683 research outputs found
How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow
Context: The success of Stack Overflow and other community-based
question-and-answer (Q&A) sites depends mainly on the will of their members to
answer others' questions. In fact, when formulating requests on Q&A sites, we
are not simply seeking for information. Instead, we are also asking for other
people's help and feedback. Understanding the dynamics of the participation in
Q&A communities is essential to improve the value of crowdsourced knowledge.
Objective: In this paper, we investigate how information seekers can increase
the chance of eliciting a successful answer to their questions on Stack
Overflow by focusing on the following actionable factors: affect, presentation
quality, and time.
Method: We develop a conceptual framework of factors potentially influencing
the success of questions in Stack Overflow. We quantitatively analyze a set of
over 87K questions from the official Stack Overflow dump to assess the impact
of actionable factors on the success of technical requests. The information
seeker reputation is included as a control factor. Furthermore, to understand
the role played by affective states in the success of questions, we
qualitatively analyze questions containing positive and negative emotions.
Finally, a survey is conducted to understand how Stack Overflow users perceive
the guideline suggestions for writing questions.
Results: We found that regardless of user reputation, successful questions
are short, contain code snippets, and do not abuse with uppercase characters.
As regards affect, successful questions adopt a neutral emotional style.
Conclusion: We provide evidence-based guidelines for writing effective
questions on Stack Overflow that software engineers can follow to increase the
chance of getting technical help. As for the role of affect, we empirically
confirmed community guidelines that suggest avoiding rudeness in question
writing.Comment: Preprint, to appear in Information and Software Technolog
Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums
Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom’s epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom’s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis
Prédiction de la détérioration du comportement à l’aide de l’apprentissage automatique
Les plateformes de médias sociaux rassemblent des individus pour interagir de manière amicale et civilisée tout en ayant des convictions et des croyances diversifiées. Certaines personnes adoptent des comportements répréhensibles qui nuisent à la sérénité et affectent négativement l’équanimité des autres utilisateurs. Certains cas de mauvaise conduite peuvent initialement avoir de petits effets statistiques, mais leur accumulation persistante pourrait entraîner des conséquences majeures et dévastatrices. L’accumulation persistante des mauvais comportements peut être un prédicteur valide des facteurs de risque de détérioration du comportement. Le problème de la détérioration du comportement n’a pas été largement étudié dans le contexte des médias sociaux.
La détection précoce de la détérioration du comportement peut être d’une importance cruciale pour éviter que le mauvais comportement des individus ne s’aggrave. Cette thèse aborde le problème de la détérioration du comportement dans le contexte des médias sociaux. Nous proposons de nouvelles méthodes basées sur l’apprentissage automatique qui (1) explorent les séquences comportementales et leurs motifs temporels pour faciliter la compréhension des comportements manifestés par les individus et (2) prédisent la détérioration du comportement à partir de combinaisons consécutives de motifs séquentiels correspondant à des comportements inappropriés. Nous menons des expériences approfondies à l’aide d’ensembles de données du monde réel et démontrons la capacité de nos modèles à prédire la détérioration du comportement avec un haut degré de précision, c’est-à -dire des scores F-1 supérieurs à 0,8. En outre, nous examinons la trajectoire de détérioration du comportement afin de découvrir les états émotionnels que les individus présentent progressivement et d’évaluer si ces états émotionnels conduisent à la détérioration du comportement au fil du temps. Nos résultats suggèrent que la colère pourrait être un état émotionnel potentiel qui pourrait contribuer substantiellement à la détérioration du comportement
Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach
Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context
Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach
Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context
Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter?
The increase in the prevalence of mental health problems has coincided with a
growing popularity of health related social networking sites. Regardless of
their therapeutic potential, On-line Support Groups (OSGs) can also have
negative effects on patients. In this work we propose a novel methodology to
automatically verify the presence of therapeutic factors in social networking
websites by using Natural Language Processing (NLP) techniques. The methodology
is evaluated on On-line asynchronous multi-party conversations collected from
an OSG and Twitter. The results of the analysis indicate that therapeutic
factors occur more frequently in OSG conversations than in Twitter
conversations. Moreover, the analysis of OSG conversations reveals that the
users of that platform are supportive, and interactions are likely to lead to
the improvement of their emotional state. We believe that our method provides a
stepping stone towards automatic analysis of emotional states of users of
online platforms. Possible applications of the method include provision of
guidelines that highlight potential implications of using such platforms on
users' mental health, and/or support in the analysis of their impact on
specific individuals
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