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.

One of today's major research trends in the field of information systems is the discovery of implicit knowledge hidden in dataset that is currently being produced at high speed, large volumes and with a wide variety of formats. Data with such features is called big data. Extracting, processing, and visualizing the huge amount of data, today has become one of the concerns of data science scholars. The impact of big data on information analysis can be traced to four different parts. The first part is data extraction and processing, the second part is data analysis, the third part is data storage, and finally the visualization of the data. In the field of big data processing, in various studies, different categories have been presented. For example, in the studies of Hashim et al., big data processing is divided into two categories. These two types are: batch and real time. These two categories of processing, which nowadays are standard in any comprehensive big data solution, also have been introduced in Abawajy studies: batch processing is related to offline processing, and real-time processing is usually used to analyze the streaming data without any need to storage of data on disk. As data flows from various sources, the data is analyzed and processed real time, for immediate insight. As today's world is rapidly changing and survival in today's competitive world requires instant decision-making based on flows of data, streaming data analysis is becoming increasingly important. On the other hand, one of the great valuable sources of streaming data is the data generated by social networks’ users such as Twitter. Social networks data sources are very rich sources for analysis as they come from the opinions and opinions of their users. As discussed earlier, and since previous studies such as Flash's studies have focused more on batch analysis (offline data), this study has attempted to investigate a variety of tools and infrastructures related to big streaming data, and finally design a real-time dashboard based on Twitter social network streaming data. The following article addresses two research questions: 1) How to design and implement a real-time dashboard based on social networks data? 2) Which different configurations are best suited for real-time dashboard analysis and visualization? In other words, the purpose of this article is to provide a solution for extracting and visualizing Twitter's social network streaming data by deleting databases, as an examples of big data real time analysis. In this research, we used Twitter streaming data as an input, Apache Storm as a processing platform and D3. js as a visualization tool. Finally, the designed dashboard was evaluated using Design of Experiment method and other statistical tests in various types of Apache Storm configurations and eventually it was proved that the dashboard is real time with an average response time for 1 minute and 30 seconds.

Today, online social media with numerous users from ordinary citizens to top government officials, organizations, artists and celebrities, etc. is one of the most important platforms for sharing information and communication. These media provide users with quick and easy access to information so that the content of shared posts has the potential to reach millions of users in a matter of seconds. Twitter is one of the most popular and practical/used online social networks for spreading information, which, while being reliable, can also, be a source for spreading unrealistic and deceptive rumors as a result can have irreversible effects on individuals and society. Recently, several studies have been conducted in the field of rumor detection and verify using models based on deep learning and machine learning methods. Previous research into rumor detection has focused more on linguistic, user, and structural features. Concerning structural features, they examined the retweet propagation graph. However, in this study, unlike the previous studies, new structural features of the reply tree and user graph in extracting rumored conversations were extracted and analyzed from different aspects. In this study, the effectiveness of new structural features related to reply tree and user graph in detecting rumored conversations in Twitter events were evaluated from different aspects. First, the structural features of the reply tree and user graph were extracted at different time intervals, and important features in these intervals were identified using the Sequential Forward Selection approach. To evaluate the usefulness of valuable new structural features, these features have been compared with consideration of linguistic and user-specific features. Experiments have shown that combining new structural features with linguistic and user-specific features increases the accuracy of the rumor detection classification. Therefore, a rumor classification algorithm based on new structural, linguistic, and user-specific features in rumor conversation detection was proposed. This algorithm performs better than the basic methods and detects rumored conversations with greater accuracy. In addition, due to the importance of the source tweet user in conversations, this user was examined and analyzed from different aspects. The results showed that most rumored conversations were started by a small number of users. Rumors can be prevented by early identification of these users on Twitter events.

Following the murder of Martyr Soleimani in 2019, the dominant system and the opponents of the Islamic resistance made significant attempts to influence and control the public opinion around the globe, particularly in the West Asian region and among Americans, using media outlets and open internet forums in an effort to persuade the world of this vile evil while defending the assassination. The next piece, which addresses the crucial problem of media representation, representation of Qassem Soleimani's murder on Twitter and Instagram of Iran International Network is how this key question is being addressed with the use of qualitative content analysis method, intelligent data gathering techniques, and netnography methodology. How did it get done? " The study's conclusion examining and expressing the opinions of the Twitter and Instagram users of the Iran International news media, highlighting the time of Martyr Soleimani's assassination, highlight many points: Inversion of the facts and reality of the resistance front through deliberate representation and the use of stereotyping. Islamic and consistent with both American statesmen's conduct and global public opinion. The Islamic Resistance Front has triumphed in armed conflict, but it has not made effective use of its resources when creating dialogue online. The resistance front must innovate in the arena of media conflict and cyberspace and switch from an offensive to a defensive approach. In fact, with the help of an imaging pioneer, the Islamic Resistance Front might seize media coverage.

Purpose: Today, social media as a multidimensional communication channel, is considered as a complete tool for dissemination news and has provided market stakeholders with access to complementary information. The purpose of this study is to investigate the relationship between disseminated earnings news on Twitter social media and the timeliness of accounting earnings information. Methodology: Data related to 345 companies listed in US S&P 500 (2016-2019), were collected and analyzed using Stata software. The tone analysis of the tweets is based on a specialized accounting vocabulary list. Results: The results showed that there is a significant relationship between the tone of disseminated earnings tweets by firms and the timeliness of the announcements of accounting earnings. Conclusion and Contribution: The interpretation of the results shows that the capability of social media as a news coverage channel allows companies to manage the earnings announcement timeliness by inevitably dissemination negative news on Twitter. Thus, despite the effective role of social media as a source of complementary awareness, this environment can be prone to strategic news dissemination by companies.

Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributing spam posts. This paper employs a combination of machine learning methods to identify spam at the tweet level. The proposed method utilizes a feature extraction framework in two stages. In the first stage, Stacked Autoencoder is used for feature extraction, and in the second stage, the extracted features from the last layer of Stacked Autoencoder are fed into the softmax layer for prediction. The proposed method is compared and evaluated against some popular methods on the Twitter Spam Detection corpus using accuracy, precision, recall, and F1-score metrics. The research results indicate that the proposed method achieves a detection of 78. 1%. Overall, the proposed method, using the majority voting approach with a hard selection in ensemble learning, outperforms CNN, LSTM, and SCCL methods in identifying spam tweets with higher accuracy.

Following the murder of Martyr Soleimani in 2019, the dominant system and the opponents of the Islamic resistance made significant attempts to influence and control the public opinion around the globe, particularly in the West Asian region and among Americans, using media outlets and open internet forums in an effort to persuade the world of this vile evil while defending the assassination. The next piece, which addresses the crucial problem of media representation, representation of Qassem Soleimani's murder on Twitter and Instagram of Iran International Network is how this key question is being addressed with the use of qualitative content analysis method, intelligent data gathering techniques, and netnography methodology. How did it get done? " The study's conclusion examining and expressing the opinions of the Twitter and Instagram users of the Iran International news media, highlighting the time of Martyr Soleimani's assassination, highlight many points: Inversion of the facts and reality of the resistance front through deliberate representation and the use of stereotyping. Islamic and consistent with both American statesmen's conduct and global public opinion. The Islamic Resistance Front has triumphed in armed conflict, but it has not made effective use of its resources when creating dialogue online. The resistance front must innovate in the arena of media conflict and cyberspace and switch from an offensive to a defensive approach. In fact, with the help of an imaging pioneer, the Islamic Resistance Front might seize media coverage.