Nowadays, the significance of monitoring stress levels and recognizing early
signs of mental illness cannot be overstated. Automatic stress detection in
text can proactively help manage stress and protect mental well-being. In
today's digital era, social media platforms reflect the psychological
well-being and stress levels within various communities. This study focuses on
detecting and analyzing stress-related posts in Reddit academic communities.
Due to online education and remote work, these communities have become central
for academic discussions and support. We classify text as stressed or not using
natural language processing and machine learning classifiers, with Dreaddit as
our training dataset, which contains labeled data from Reddit. Next, we collect
and analyze posts from various academic subreddits. We identified that the most
effective individual feature for stress detection is the Bag of Words, paired
with the Logistic Regression classifier, achieving a 77.78% accuracy rate and
an F1 score of 0.79 on the DReaddit dataset. This combination also performs
best in stress detection on human-annotated datasets, with a 72% accuracy rate.
Our key findings reveal that posts and comments in professors Reddit
communities are the most stressful, compared to other academic levels,
including bachelor, graduate, and Ph.D. students. This research contributes to
our understanding of the stress levels within academic communities. It can help
academic institutions and online communities develop measures and interventions
to address this issue effectively.

通过自然语言处理和机器学习分类器，本研究以 Reddit 学术社群为对象，检测和分析与压力相关的帖子，发现 Bag of Words 与 Logistic Regression 分类器结合在压力检测上效果最佳，准确率达到 77.78%，F1 得分为 0.79。研究结果表明，与本科生、研究生和博士生等学术层次相比，教授在 Reddit 社群中的帖子和评论是最具压力的。本研究对于了解学术社群压力水平具有重要意义，可以帮助学术机构和在线社群有效地解决这一问题。

Reddit 学术社区中与压力相关的帖子的检测与分析

Detection and Analysis of Stress-Related Posts in Reddit Acamedic  Communities

Preprints play an increasingly critical role in academic communities. There
are many reasons driving researchers to post their manuscripts to preprint
servers before formal submission to journals or conferences, but the use of
preprints has also sparked considerable controversy, especially surrounding the
claim of priority. In this paper, a case study of computer science preprints
submitted to arXiv from 2008 to 2017 is conducted to quantify how many
preprints have eventually been printed in peer-reviewed venues. Among those
published manuscripts, some are published under different titles and without an
update to their preprints on arXiv. In the case of these manuscripts, the
traditional fuzzy matching method is incapable of mapping the preprint to the
final published version. In view of this issue, we introduce a semantics-based
mapping method with the employment of Bidirectional Encoder Representations
from Transformers (BERT). With this new mapping method and a plurality of data
sources, we find that 66% of all sampled preprints are published under
unchanged titles and 11% are published under different titles and with other
modifications. A further analysis was then performed to investigate why these
preprints but not others were accepted for publication. Our comparison reveals
that in the field of computer science, published preprints feature adequate
revisions, multiple authorship, detailed abstract and introduction, extensive
and authoritative references and available source code.

计算机科学领域的预印本在 arXiv 上发布后，通过引入基于语义的映射方法（使用了 BERT），我们发现所调研的预印本中的 66％发表时标题未变且 11％具有改变标题和其他修改。进一步的比较研究揭示了在计算机科学领域中，被发表的预印本具有充分的修订、多个作者、详细的摘要和导言、广泛且权威的参考文献以及可用的源代码。