Purpose: A considerable amount of scholarly articles are disseminated on TWITTER. The current research aims to analyze the dissemination of around 45 million tweets of scientific papers as a sample of altmetric capabilities in big data analysis on social web. Methodology: The current study is an applied research in terms of objectives while it is a descriptive and survey in terms of data analysis and conducted using altmetric indicators. Research sample consisted of all 44, 828, 322 tweets and retweets of more than 676, 400 papers obtained from Altmetric Explorer at the time of data collection in November 2017. Findings: Results of the study revealed that %78. 8 of all mentions of scientific papers in social web were on TWITTER. Temporal investigation of tweets showed a considerable increase in the number of scientific papers which have been shared on TWITTER during the time under consideration. The highest share of tweets and the most number of tweeters were found to be from the United States, The UK and Australia. Moreover, research output published in medical science as well as multidisciplinary sciences journals were tweeted more frequently than other research areas. Conclusion: TWITTER has been identified as the most important social media platform and altmetric source for sharing of research output. It has a large potential to measure the social impact of scientific output.

The proliferation of false information on social media has profound negative impacts across various aspects of people's lives. To mitigate these effects, numerous studies have focused on developing automated factchecking systems aimed at enhancing the accuracy and reliability of news and information. Claim detection, recognized as the initial stage in constructing such systems, has been explored in several languages. In our paper, we introduce a corpus of Persian tweets annotated with 11 labels derived from linguistic analysis, representing different types of claims. Additionally, we establish a baseline claim detection model to assess the dataset. This study frames claim detection as a classification task and employs a transformer-based approach to train a multi-label classifier capable of identifying various types of claims in Persian texts.

TWITTER is a social media that provides widespread engagement and interaction possibilities. In the last decade, the media has attracted the attention of politicians, especially diplomats, and has become a platform for the reflection of their priorities, values, and ideas. The use of this infrastructure has created a new type of diplomacy called "Twiplomacy", and more than 92 percent of the member states of the United Nations have a user account and represent their identity and their country on TWITTER. In Iran, politicians and diplomats have not been able to keep up with this trend to have an active presence. By using the Ontological Security Theory, this essay seeks to explore how Iranian officials, especially diplomats, can represent the identity and norms of the foreign policy of the Islamic Republic of Iran on TWITTER. The main question is how Iranian politicians narrate themselves on TWITTER. In order to investigate this question, in addition to TWITTER of Iran's Foreign Minister, Donald Trump and Benjamin Netanyahu's tweets were also considered as the main rivals of the Islamic Republic of Iran’s ontological narration on foreign policy. Accordingly, the following criteria were summarized as Iran's foreign policy narratives: First, Iran as a security maker of the region; second, Iran as an economically developing state; third, Iran as a compliant with international obligations; and last, an independent Iran.

One of the most important topics that social networks-TWITTER-have paid attention to is violence against women in Iran. The most important theoretical support in the emergence of these campaigns is the reflection of the texts about violence against women. One of the most important factors in the emergence of such waves is the important activists in the field of these social networks, who use their followers to reflect these contents. The most important campaigns in terms of the number of tweets and retweets was the "Girls of the Revolution" campaign. The aim of the research is to analyze the performance of TWITTER in reflecting the content of violence against women in Iran based on the most important campaigns that have emerged in the last decade. This issue was analyzed based on the theoretical foundations of types of violence and networked movements. The research method is the content analysis of tweets and hashtags related to campaigns from 2016 to 2016. The research results showed that the most important themes are about social and political violence against women. In this regard, some campaigns and hashtags have caused insecurities in the society the most important of which was the campaign of the girls of Enghelab Street in terms of the number of tweets and retweets. the analysis of the themes of tweets showed the absence of news, posts and tweets of people related to campaigns and hashtags.