Twitter: More than Tweets for Undergraduate Student Researchers

Reyna, Nathan S.; Pruett, Christin L.; Morrison, Mike; Fowler, Jennifer; Pandey, Sumali; Hensley, Lori L.

doi:10.1128/jmbe.00326-21

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…This is also parallel with the findings of previous study that showed Twitter can improve the spread of information to the students which was conducted on a different topic (Al-Khalifa et al, 2022). Another benefit of using Twitter is the ability to share messages with other users or followers (Reyna et al, 2022), which in this case, refer to the students. This feature allows lecturers to send messages direct and on-point within the words limit.…”

Section: Using Twitter To Decimate Information Outside Of the Classroomsupporting

confidence: 86%

Exploring the Usage of Twitter in Higher Education

Madli¹,

Janin²,

Loong³

et al. 2023

IJARBSS

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The use of technology in the context of higher education is not a novel idea as lecturers and students have used different types of technology such as smartphones, software, and social media. In recent years, social media like Twitter have been extensively used to decimate information about learning. However, there is a global disparity between the use of social media sites like Twitter for higher education; some countries use it extensively, while for some, its use is a rarity. This situation is evident in Malaysia, where there is a low percentage of students and lecturers using Twitter in teaching and learning. Furthermore, there are limited studies related to Twitter in the context of higher education in Malaysia. Based on this gap, this study aims to devise a strategy for using Twitter in Malaysia's higher education context. This study discusses some of the strategies that can be implemented in conducting learning inside or outside the class. In addition, the study's findings can benefit many education stakeholders by exploring ways to optimise the use of Twitter in Higher Education Institutions (HEIs).

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Section: Using Twitter To Decimate Information Outside Of the Classroomsupporting

confidence: 86%

Exploring the Usage of Twitter in Higher Education

Madli¹,

Janin²,

Loong³

et al. 2023

IJARBSS

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“…Moreover, we did not attempt to separate different types of Twitter use, e.g. for expression opinions and debates versus more structured activities, such as education [16]. We also made no effort to judge the veracity, tone, sentiment and constructiveness (or lack thereof) of the tweets [17], since this is extremely difficult to do for people covering such a wide range of expertise.…”

Section: Discussionmentioning

confidence: 99%

Twitter and Mastodon presence of highly-cited scientists

Siebert

Siena

Ioannidis

2023

Preprint

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Social media platforms have an increasing influence in biomedical and other disciplines of science and public health. While Twitter has been a popular platform for scientific communication, changes in ownership have led some users to consider migrating to other platforms such as Mastodon. We aimed to investigate how many top-cited scientists are active on these social media platforms, the magnitude of the migration to Mastodon, and correlates of Twitter presence. A random sample of 900 authors was examined among those who are at the top-2% of impact based on a previously validated composite citation indicator using Scopus data. Searches for their personal Twitter accounts were performed in early December 2022, and re-evaluations were performed at 2 weeks, 4 weeks, and 2 months (February 6, 2023). 262/900 (29.1%) of highly-cited scholars had Twitter accounts, and only 9/800 (1%) had Mastodon accounts. Female gender, North American and Australia locations, younger publication age, and clinical medicine or social science expertise correlated with higher percentages of Twitter use. The vast majority of highly-cited author users of Twitter had few followers and tweets. Only 6 had more than 10,000 followers and none had more than 100,000. One limitation of our study is that it is possible that some accounts, especially with Mastodon, could not be detected. However, the study suggests that Twitter remains the preferred social media platform for highly-cited authors, and Mastodon has not yet challenged Twitter's dominance. Moreover, most highly-cited scientists with Twitter accounts have limited presence in this medium.

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“…These fragments of information have grown into informational treasure troves, providing numerous opportunities for analyzing consumer behavior, which is particularly useful in predicting product sales [2], stock market trends [3], and election results [4]. More than 300 million active users use X, which is one of the most widely used microblogging services [5], [6]. Due to its importance in understanding user's feelings and insolences, X Sentiment Analysis has received a lot of attention [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

Suman,

Varshney,

Chouhan

et al. 2024

IJCDS

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X (formerly Twitter) has become a vital source of information on various variety of social, political, and economic concerns, as a consequence of its growth and popularity which has resulted in an enormous number of people sharing their opinions on a wide range of areas. To determine people's emotions about the Russia-Ukraine war (RUW), this study examines trends in English-language tweets. In this work, we have engaged 34 countries to tweet opinions that produce a strong perception of the people about the war and message to the world what people famine from the countries and that affects their lives. To analyze positive and negative emotions in tweets, which are represented by hope and fear, the LSTM-CNN model is based on deep learning. A time series is calculated that correlates with the rate of recurrence of negative and positive tweets in different nations. Additionally, an approach based on the average of the neighborhood has been used for modelling and grouping the time series of various countries. The clustering method gives results as significant information, how people feel about this dispute and share their opinions about RUW is approached. When compare on different models on overall data that the 96% accuracy is Achieved by the LSTM-CNN model. 97.09% accuracy, is achieved when the comparing the tweets from the cluster 1 countries. When comparing the tweets from the cluster 2 countries the 99% accuracy, is achieved. 97% accuracy is achieved by comparing the tweets from cluster 3 countries. 97% accuracy, is achieved when the comparing the tweets from cluster 4 countries. 96% accuracy is achieved by the LSTM-CNN model when the comparing the tweets from cluster 5 countries by the different models. This research study helps the uninfluenced press members to have an impartial source of information for their reports and articles.

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Twitter: More than Tweets for Undergraduate Student Researchers

Cited by 9 publications

References 7 publications

Exploring the Usage of Twitter in Higher Education

Exploring the Usage of Twitter in Higher Education

Twitter and Mastodon presence of highly-cited scientists

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

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