3,930 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
Comparing and Combining Sentiment Analysis Methods
Several messages express opinions about events, products, and services,
political views or even their author's emotional state and mood. Sentiment
analysis has been used in several applications including analysis of the
repercussions of events in social networks, analysis of opinions about products
and services, and simply to better understand aspects of social communication
in Online Social Networks (OSNs). There are multiple methods for measuring
sentiments, including lexical-based approaches and supervised machine learning
methods. Despite the wide use and popularity of some methods, it is unclear
which method is better for identifying the polarity (i.e., positive or
negative) of a message as the current literature does not provide a method of
comparison among existing methods. Such a comparison is crucial for
understanding the potential limitations, advantages, and disadvantages of
popular methods in analyzing the content of OSNs messages. Our study aims at
filling this gap by presenting comparisons of eight popular sentiment analysis
methods in terms of coverage (i.e., the fraction of messages whose sentiment is
identified) and agreement (i.e., the fraction of identified sentiments that are
in tune with ground truth). We develop a new method that combines existing
approaches, providing the best coverage results and competitive agreement. We
also present a free Web service called iFeel, which provides an open API for
accessing and comparing results across different sentiment methods for a given
text.Comment: Proceedings of the first ACM conference on Online social networks
(2013) 27-3
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