Increasing the number of cyclists, whether for general transport or
recreation, can provide health improvements and reduce the environmental impact
of vehicular transportation. However, the public's perception of cycling may be
driven by the ideologies and reporting standards of news agencies. For
instance, people may identify cyclists on the road as "dangerous" if news
agencies overly report cycling accidents, limiting the number of people that
cycle for transportation. Moreover, if fewer people cycle, there may be less
funding from the government to invest in safe infrastructure. In this paper, we
explore the perceived perception of cyclists within news headlines. To
accomplish this, we introduce a new dataset, "Bike Frames", that can help
provide insight into how headlines portray cyclists and help detect
accident-related headlines. Next, we introduce a multi-task (MT) regularization
approach that increases the detection accuracy of accident-related posts,
demonstrating improvements over traditional MT frameworks. Finally, we compare
and contrast the perceptions of cyclists with motorcyclist-related headlines to
ground the findings with another related activity for both male- and
female-related posts. Our findings show that general news websites are more
likely to report accidents about cyclists than other events. Moreover,
cyclist-specific websites are more likely to report about accidents than
motorcycling-specific websites, even though there is more potential danger for
motorcyclists. Finally, we show substantial differences in the reporting about
male vs. female-related persons, e.g., more male-related cyclists headlines are
related to accidents, but more female-related motorcycling headlines about
accidents. WARNING: This paper contains descriptions of accidents and death.

通过一个新的数据集 “自行车框架”，本研究探讨新闻标题中对骑行者的观感，引入了多任务正则化方法提高事故相关帖子的检测准确性，并通过与摩托车相关标题的比较来证实结果，发现一般新闻网站更有可能报道自行车事故，女性摩托骑士更容易有关事故的标题。

自行车车架：了解新闻中对骑行者的隐含描绘

Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News

As technology grows faster, the news spreads through social media. In order
to attract more readers and acquire additional profit, some news agencies
reproduce massive news in a more appealing manner. Therefore, it is essential
to accurately predict whether a news article is from official news agencies.
This work develops a headline classification based on Convoluted Neural Network
to determine credibility of a news article. The model primarily focuses on
investigating key factors from headlines. These factors include word
segmentation, part-of-speech tags, and sentiment features. With integrating
these features into the proposed classification model, the demonstrated
evaluation achieves 93.99% for accuracy.

该研究基于卷积神经网络开发了一个标题分类器，主要通过调查标题的词语分割、词性标注和情感特征等关键因素，以确定新闻文章的可信度，并将这些特征集成到分类模型中。经过实验证明，该模型的准确度可达到 93.99%。