With the recent surge and exponential growth of social media usage,
scrutinizing social media content for the presence of any hateful content is of
utmost importance. Researchers have been diligently working since the past
decade on distinguishing between content that promotes hatred and content that
does not. Traditionally, the main focus has been on analyzing textual content.
However, recent research attempts have also commenced into the identification
of audio-based content. Nevertheless, studies have shown that relying solely on
audio or text-based content may be ineffective, as recent upsurge indicates
that individuals often employ sarcasm in their speech and writing. To overcome
these challenges, we present an approach to identify whether a speech promotes
hate or not utilizing both audio and textual representations. Our methodology
is based on the Transformer framework that incorporates both audio and text
sampling, accompanied by our very own layer called "Attentive Fusion". The
results of our study surpassed previous state-of-the-art techniques, achieving
an impressive macro F1 score of 0.927 on the Test Set.

通过结合音频和文本表征，利用 Transformer 方法和自主开发的 “Attentive Fusion” 层，本研究提出了一种识别言论是否宣扬仇恨的方法，取得了 0.927 的宏 F1 得分，超过了此前最先进的技术。