Online discourse covers a wide range of topics and many actors tailor their
content to impact online discussions through carefully crafted messages and
targeted campaigns. Yet the scale and diversity of online media content make it
difficult to evaluate the impact of a particular message. In this paper, we
present a new technique that leverages semantic similarity to quantify the
change in the discussion after a particular message has been published. We use
a set of press releases from environmental organisations and tweets from the
climate change debate to show that our novel approach reveals a heavy-tailed
distribution of response in online discourse to strategic communications.

本文提出了一种利用语义相似性量化特定信息发布后讨论变化的新技术，并使用环保组织的新闻稿和气候变化辩论的推文来展示我们的新方法揭示了在线话语对战略传播的不同响应规律。

使用语义相似性和文本嵌入测量战略传播的社交媒体回响

Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications

We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.

研究人员提出了一种新型的对话系统，利用动态知识图谱嵌入技术，让两个带有私有知识的代理人进行协作对话，旨在达到某个共同目标，并经过了自动化和人工实验的验证，结果表明该模型与基线模型相比，不仅更有效地实现了目标，而且更加人性化。