Social media platforms influence the way political campaigns are run and
therefore they have become an increasingly important tool for politicians to
directly interact with citizens. Previous elections in various countries have
shown that social media data may significantly impact election results. In this
study, we aim to predict the vote shares of parties participating in the 2023
elections in Turkey by combining social media data from various platforms
together with traditional polling data. Our approach is a volume-based approach
that considers the number of social media interactions rather than content. We
compare several prediction models across varying time windows. Our results show
that for all time windows, the ARIMAX model outperforms the other algorithms.

通过结合社交媒体数据及传统投票数据，我们采用基于交互量的方法，比较多种算法在不同时窗口下预测土耳其 2023 年选举党派得票份额，结果发现，在所有时间窗口下，ARIMAX 模型表现最佳。

利用社交媒体数据预测 2023 年土耳其总统选举结果

Prediction of the 2023 Turkish Presidential Election Results Using  Social Media Data

Social media platforms are currently the main channel for political
messaging, allowing politicians to target specific demographics and adapt based
on their reactions. However, making this communication transparent is
challenging, as the messaging is tightly coupled with its intended audience and
often echoed by multiple stakeholders interested in advancing specific
policies. Our goal in this paper is to take a first step towards understanding
these highly decentralized settings. We propose a weakly supervised approach to
identify the stance and issue of political ads on Facebook and analyze how
political campaigns use some kind of demographic targeting by location, gender,
or age. Furthermore, we analyze the temporal dynamics of the political ads on
election polls.

本文从弱监督的角度，探讨 Facebook 中的政治广告立场、问题、以及地理、性别、年龄等不同人群的定向投放，并且分析了选举时期政治广告的时态动态。

Facebook 政治活动分析的弱监督学习

Weakly Supervised Learning for Analyzing Political Campaigns on Facebook

Political campaigns are full of political ads posted by candidates on social
media. Political advertisements constitute a basic form of campaigning,
subjected to various social requirements. We present the first publicly open
dataset for detecting specific text chunks and categories of political
advertising in the Polish language. It contains 1,705 human-annotated tweets
tagged with nine categories, which constitute campaigning under Polish
electoral law. We achieved a 0.65 inter-annotator agreement (Cohen's kappa
score). An additional annotator resolved the mismatches between the first two
annotators improving the consistency and complexity of the annotation process.
We used the newly created dataset to train a well established neural tagger
(achieving a 70% percent points F1 score). We also present a possible direction
of use cases for such datasets and models with an initial analysis of the
Polish 2020 Presidential Elections on Twitter.

在社交媒体上，政治竞选充斥着政治广告。该研究提出了一种公开可用的数据集，用于检测特定文本块和政治广告类别的内容，特定文本块和政治广告类别是符合波兰选举法的竞选基本形式。数据集包含 1,705 条人工标注的推文，标有九个类别。该数据集被用来训练神经标记器，并用于对波兰 2020 年总统选举在 Twitter 上的初步分析。