Understanding the social evolution of the Java community in Stack Overflow: A 10-year study of developer interactions

Blanco, Guillermo; Pérez-López, Roi; Fdez-Riverola, Florentino; Lourenço, Anália

doi:10.1016/j.future.2019.12.021

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…The dataset of Stack Exchange, a popular SQA, containing questions and answers posted from August 2008 to December 2016 was used for this work. It contains several XML files related to different attributes of the post (questions and answers) 1 .…”

Section: Methodsmentioning

confidence: 99%

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Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

Roy

Ahmad

Singh

et al. 2022

IETE Journal of Research

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Social Question Answering sites (SQAs) are online platforms that allow Internet users to ask questions, and obtain answers from others in the community. SQAs have been marred by the problem of low-quality answers. Worryingly, answer quality on SQAs have been reported to be following a downward trajectory in recent years. To this end, existing research has predominantly focused on finding the best answer, or identifying high-quality answers among the available responses.However, such scholarly efforts have not reduced the volume of low-quality answers on SQAs. Therefore, the goal of this research is to extract features in order to weed out low-quality answers as soon as they are posted on SQAs. Data from Stack Exchange was used to carry out the investigation. Informed by the literature, 26 features were extracted. Thereafter, machine learning algorithms were implemented that could correctly identify 85% to 96% of low-quality answers. The key contribution of this research is the development of a system to detect subpar answers on the fly at the time of posting. It is intended to be used as an early warning system that warns users about answer quality at the point of posting. Lee et al. [27] assigned a score to each voter to capture the level of agreement among those who up-voted or down-voted answers. This voting score was used as a feature to predict the best answer. These works typically use machine learning-

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Section: Methodsmentioning

confidence: 99%

“…Social Question Answering sites (SQAs) refer to online platforms that allow Internet users to ask questions on any topic, and obtain answers from others in the community [1][2][3]. These platforms leverage the wisdom of crowds [4], and facilitate prompt information seeking among the masses [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

Roy

Ahmad

Singh

et al. 2022

IETE Journal of Research

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“…In this regard, it has been reported that females, who encounter other members of the same gender, are more likely to engage sooner than those who do not in Stack Overflow [32]. Another significant discovery discloses a stronger tendency among women to post more questions, whereas males to yield more answers, resulting in fewer thumb-ups for them, giving raise to lower average reputation scores for females [33]- [35]. Working under these findings, they designed a reputation strategy to lessen the gender gap that rewards points for publishing questions and answers to the same level.…”

Section: B Gender Identification On Cqa Platformsmentioning

confidence: 99%

Textual Pre-Trained Models for Gender Identification Across Community Question-Answering Members

Schwarzenberg¹,

Figueroa²

2023

IEEE Access

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Promoting engagement and participation is vital for online social networks such as community Question-Answering (cQA) sites. One way of increasing the contribution of their members is by connecting their content with the right target audience. To achieve this goal, demographic analysis is pivotal in deciphering the interest of each community fellow. Indeed, demographic factors such as gender are fundamental in reducing the gender disparity across distinct topics. This work assesses the classification rate of assorted state-of-the-art transformer-based models (e.g., BERT and FNET) on the task of gender identification across cQA fellows. For this purpose, it benefited from a massive text-oriented corpus encompassing 548,375 member profiles including their respective fullquestions, answers and self-descriptions. This assisted in conducting large-scale experiments considering distinct combinations of encoders and sources. Contrary to our initial intuition, in average terms, selfdescriptions were detrimental due to their sparseness. In effect, the best transformer models achieved an AUC of 0.92 by taking full-questions and answers into account (i.e., DeBERTa and MobileBERT). Our qualitative results reveal that fine-tuning on user-generated content is affected by pre-training on clean corpora, and that this adverse effect can be mitigated by correcting the case of words.

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“…The questions of Q&A communities are generally tagged with program languages such as Java and Python [ 33 ]. These tags partition a Q&A community into different language communities where intra-group interactions are more frequent than inter-group interactions [ 34 ], reflecting the aggregation of users. This structure of the Q&A community enables us to explore more conveniently and accurately the characteristics of the whole Q&A community with its certain language community [ 35 ].…”

Section: Normassmentioning

confidence: 99%

NorMASS: A normative MAS-based modeling approach for simulating incentive mechanisms of Q&A communities

et al. 2023

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Incentive mechanisms steer users in Q&A communities to achieve community goals, which need to be cautiously reviewed and revised before actual industrial application. Simulating incentive mechanisms is significant for predicting how changes in incentive mechanisms will affect community emergence, such as user answering patterns. However, due to the complexity of Q&A communities, the challenge faced by simulating incentive mechanisms lies in the difficulty of establishing micro-macro connections in the communities to simulate their emergence. To fill this gap, this paper proposes a Normative Multi-Agent System based Simulation (NorMASS) approach to simulate community emergence. The NorMASS models a Q&A community as a normative multi-agent system and adopts agents to formally express community users. Moreover, the approach provides an open-source simulator with a data generator to simulate community emergence. An evaluation of the NorMASS comparing simulation emergence with the counterpart of an actual community demonstrates that the proposed approach provides an effective solution for simulating incentive mechanisms of Q&A communities, with a similarity of 80% or above.

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Understanding the social evolution of the Java community in Stack Overflow: A 10-year study of developer interactions

Cited by 12 publications

References 28 publications

Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

Textual Pre-Trained Models for Gender Identification Across Community Question-Answering Members

NorMASS: A normative MAS-based modeling approach for simulating incentive mechanisms of Q&A communities

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