Along with factors such as cancer, blood pressure, street accidents and
stroke, suicide has been one of Iran main causes of death. One of the main
reasons for suicide is psychological stressors. Identifying psychological
stressors in an at risk population can help in the early prevention of suicidal
and suicidal behaviours. In recent years, the widespread popularity and flow of
real time information sharing of social media have allowed for potential early
intervention in large scale and even small scale populations. However, some
automated approaches to extract psychiatric stressors from Twitter have been
presented, but most of this research has been for non Persian languages. This
study aims to investigate the techniques of detecting psychological stress
related to suicide from Persian tweets using learning based methods. The
proposed capsule based approach achieved a binary classification accuracy of
0.83.

通过学习方法从波斯推文中检测与自杀相关的心理压力，研究提出的胶囊式方法实现了二元分类准确率达到 0.83。

基于胶囊融合的从 Twitter 提取自杀的心理压力因素

CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide  from twitter

The Covid-19 pandemic had an enormous effect on our lives, especially on
people's interactions. By introducing Covid-19 vaccines, both positive and
negative opinions were raised over the subject of taking vaccines or not. In
this paper, using data gathered from Twitter, including tweets and user
profiles, we offer a comprehensive analysis of public opinion in Iran about the
Coronavirus vaccines. For this purpose, we applied a search query technique
combined with a topic modeling approach to extract vaccine-related tweets. We
utilized transformer-based models to classify the content of the tweets and
extract themes revolving around vaccination. We also conducted an emotion
analysis to evaluate the public happiness and anger around this topic. Our
results demonstrate that Covid-19 vaccination has attracted considerable
attention from different angles, such as governmental issues, safety or
hesitancy, and side effects. Moreover, Coronavirus-relevant phenomena like
public vaccination and the rate of infection deeply impacted public emotional
status and users' interactions.

本文利用数据挖掘技术和情感分析工具，分析了伊朗社交媒体上关于 COVID-19 疫苗的公众舆情，结果显示该话题广泛关注，从政府问题、安全到犹豫和副作用等各个角度得到了探讨，公共接种和感染率等相关现象深刻地影响了公众情绪和用户互动。

关于 Covid-19 疫苗的波斯语推文的大规模分析

A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination

Iran, along with China, South Korea, and Italy was among the countries that
were hit hard in the first wave of the COVID-19 spread. Twitter is one of the
widely-used online platforms by Iranians inside and abroad for sharing their
opinion, thoughts, and feelings about a wide range of issues. In this study,
using more than 530,000 original tweets in Persian/Farsi on COVID-19, we
analyzed the topics discussed among users, who are mainly Iranians, to gauge
and track the response to the pandemic and how it evolved over time. We applied
a combination of manual annotation of a random sample of tweets and topic
modeling tools to classify the contents and frequency of each category of
topics. We identified the top 25 topics among which living experience under
home quarantine emerged as a major talking point. We additionally categorized
broader content of tweets that shows satire, followed by news, is the dominant
tweet type among the Iranian users. While this framework and methodology can be
used to track public response to ongoing developments related to COVID-19, a
generalization of this framework can become a useful framework to gauge Iranian
public reaction to ongoing policy measures or events locally and
internationally.

本研究通过对近 530,000 条使用波斯语发布的推文进行内容分类和频率统计，分析伊朗公众对 COVID-19 的反应和演变趋势。其中，居家隔离生活经验是讨论的主要话题之一。此方法还可用于检测伊朗公众对国内外相关政策和事件的反应。