The abstract outlines the problem of toxic comments on social media
platforms, where individuals use disrespectful, abusive, and unreasonable
language that can drive users away from discussions. This behavior is referred
to as anti-social behavior, which occurs during online debates, comments, and
fights. The comments containing explicit language can be classified into
various categories, such as toxic, severe toxic, obscene, threat, insult, and
identity hate. This behavior leads to online harassment and cyberbullying,
which forces individuals to stop expressing their opinions and ideas. To
protect users from offensive language, companies have started flagging comments
and blocking users. The abstract proposes to create a classifier using an
Lstm-cnn model that can differentiate between toxic and non-toxic comments with
high accuracy. The classifier can help organizations examine the toxicity of
the comment section better.

研究讨论了社交媒体平台上存在的有害评论问题，提出使用 Lstm-cnn 模型构建分类器，以高精度区分有毒和无毒评论，并帮助组织更好地检查评论区的有害性。

使用机器学习模型对社交媒体毒性评论进行分类

Classification of social media Toxic comments using Machine learning  models

Anti-social behaviors in social media can happen both at user and community
levels. While a great deal of attention is on the individual as an 'aggressor,'
the banning of entire Reddit subcommunities (i.e., subreddits) demonstrates
that this is a multi-layer concern. Existing research on inter-community
conflict has largely focused on specific subcommunities or ideological
opponents. However, antagonistic behaviors may be more pervasive and integrate
into the broader network. In this work, we study the landscape of conflicts
among subreddits by deriving higher-level (community) behaviors from the way
individuals are sanctioned and rewarded. By constructing a conflict network, we
characterize different patterns in subreddit-to-subreddit conflicts as well as
communities of 'co-targeted' subreddits. By analyzing the dynamics of these
interactions, we also observe that the conflict focus shifts over time.

本文研究了 Reddit 网站中亚社交行为和其对社区的影响，利用惩罚和奖励方式推导出不同层级的争议，揭示了不同 subreddits 之间的冲突模式以及互为目标的 communities，并发现这些冲突的焦点会随着时间而发生转移。