The quantitative analysis of political ideological positions is a difficult
task. In the past, various literature focused on parliamentary voting data of
politicians, party manifestos and parliamentary speech to estimate political
disagreement and polarization in various political systems. However previous
methods of quantitative political analysis suffered from a common challenge
which was the amount of data available for analysis. Also previous methods
frequently focused on a more general analysis of politics such as overall
polarization of the parliament or party-wide political ideological positions.
In this paper, we present a method to analyze ideological positions of
individual parliamentary representatives by leveraging the latent knowledge of
LLMs. The method allows us to evaluate the stance of politicians on an axis of
our choice allowing us to flexibly measure the stance of politicians in regards
to a topic/controversy of our choice. We achieve this by using a fine-tuned
BERT classifier to extract the opinion-based sentences from the speeches of
representatives and projecting the average BERT embeddings for each
representative on a pair of reference seeds. These reference seeds are either
manually chosen representatives known to have opposing views on a particular
topic or they are generated sentences which where created using the GPT-4 model
of OpenAI. We created the sentences by prompting the GPT-4 model to generate a
speech that would come from a politician defending a particular position.

利用 LLM 的潜在知识，本文提出了一种分析个别国会代表意识形态立场的方法，通过使用经过微调的 BERT 分类器从代表的演讲中提取基于意见的句子，并将每个代表的平均 BERT 嵌入投影到一对参考种子上，这些参考种子是在特定话题上具有相对立观点的已知代表，或者使用 OpenAI 的 GPT-4 模型生成的句子。

L (u) PIN: 基于 LLM 的政治意识形态实时预测

L(u)PIN: LLM-based Political Ideology Nowcasting

Social platforms such as Gab and Parler, branded as `free-speech' networks,
have seen a significant growth of their user base in recent years. This
popularity is mainly attributed to the stricter moderation enforced by
mainstream platforms such as Twitter, Facebook, and Reddit. In this work we
provide the first large scale analysis of hate-speech on Parler.
We experiment with an array of algorithms for hate-speech detection,
demonstrating limitations of transfer learning in that domain, given the
illusive and ever changing nature of the ways hate-speech is delivered. In
order to improve classification accuracy we annotated 10K Parler posts, which
we use to fine-tune a BERT classifier. Classification of individual posts is
then leveraged for the classification of millions of users via label
propagation over the social network. Classifying users by their propensity to
disseminate hate, we find that hate mongers make 16.1\% of Parler active users,
and that they have distinct characteristics comparing to other user groups. We
find that hate mongers are more active, more central and express distinct
levels of sentiment and convey a distinct array of emotions like anger and
sadness. We further complement our analysis by comparing the trends discovered
in Parler and those found in Gab.
To the best of our knowledge, this is among the first works to analyze hate
speech in Parler in a quantitative manner and on the user level, and the first
annotated dataset to be made available to the community.

本研究使用各种算法对 Parler 平台上的仇恨言论进行了大规模分析，并使用标注的数据集对用户进行了分类，发现 16.1％的活跃用户是仇恨言论传播者，并且他们在活动性、中心性、情感表达和表达的不同种类等方面具有明显的特征，这是对 Parler 平台上的仇恨言论进行的首项量化和用户层面分析研究，同时还提供了可供研究社区使用的标注数据集。

极端化：比较分析 Parler 和 Gab 中的仇恨言论

Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab

Machine translation systems with inadequate document understanding can make
errors when translating dropped or neutral pronouns into languages with
gendered pronouns (e.g., English). Predicting the underlying gender of these
pronouns is difficult since it is not marked textually and must instead be
inferred from coreferent mentions in the context. We propose a novel
cross-lingual pivoting technique for automatically producing high-quality
gender labels, and show that this data can be used to fine-tune a BERT
classifier with 92% F1 for Spanish dropped feminine pronouns, compared with
30-51% for neural machine translation models and 54-71% for a non-fine-tuned
BERT model. We augment a neural machine translation model with labels from our
classifier to improve pronoun translation, while still having parallelizable
translation models that translate a sentence at a time.

本研究提出了一种基于跨语言枢轴技术的新颖方法，用于自动生成高质量的性别标签，并展示了这些数据可用于微调 BERT 分类器，该分类器对于西班牙语中丢失的女性代词具有 92％的 F1，相比之下，神经机器翻译模型和非微调 BERT 模型分别为 30-51% 和 54-71％。我们使用来自我们分类器的标签来增强神经机器翻译模型以改进代词翻译，同时仍具有可并行化的翻译模型，可以逐句翻译。