Which Hashtag to use? Building a Hashtag recommender system and understanding the textual features surrounding Hashtags

Hashtags are community-based tags on twitter that are used to annotate tweets and make them findable. To make a user's participation on the social platform more relevant, recommending a hashtag would help a user participate better. This study is an attempt to build a recommender system for hashtag recommendation, and to further study the textual features around hashtags, which assist in their retrieval. The suggested system performs better for tweets with longer text; those with a URL, with multiple hashtags and those that have user mentions.Master of Science in Information Scienc

CRISIS COMMUNICATION DURING HEALTH CRISES: THE CASE OF CANADIAN OFFICIALS’ SOCIAL MEDIA PRESENCE DURING THE COVID-19 PANDEMIC

To effectively manage a health crisis, citizens need to have shared Situational Awareness (SA) of the crisis. This study proposes that the public draws upon shared mental models of the crisis to achieve shared SA. Declarative, procedural, and strategic knowledge bases comprise the essential aspects of shared mental models of mission-critical situations like the COVID-19 pandemic. Therefore, public officials must provide a constant flow of crisis declarative, procedural, and strategic knowledge on social media. This study investigates Canadian officials’ presence on Twitter during the COVID-19 pandemic. Analyzing a dataset of 213,089 Canadian officials’ tweets shows that their presence was either for health crisis management (73.26%) or crisis-related topics (46.66%). Declarative (72.03%), procedural (38.1%), and strategic knowledge (30.18%) comprised 96% of the health crisis management tweets. This study informs research and practice by analyzing the essential role of knowledge types in creating a shared SA in managing health crises

Deep learning based hashtag recommendation system for multimedia data

This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural network. This methodology is based on frequent pattern extraction methods. The hashtags of the tweets are then learned using the convolution neural network that was applied to the collection of batches of tweets. In addition, a pruning approach should ensure that the learning process proceeds properly by reducing the number of common patterns. Besides, the evolutionary algorithm is involved to extract the optimal parameters of the deep learning model used in the learning process. This is achieved by using a genetic algorithm that learns the hyper-parameters of the deep architecture. The effectiveness of our methodology has been demonstrated in a series of detailed experiments on a set of Twitter archives. From the results of the experiments, it is clear that the proposed method is superior to the baseline methods in terms of efficiency.publishedVersio

Deep learning based hashtag recommendation system for multimedia data

This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural network. This methodology is based on frequent pattern extraction methods. The hashtags of the tweets are then learned using the convolution neural network that was applied to the collection of batches of tweets. In addition, a pruning approach should ensure that the learning process proceeds properly by reducing the number of common patterns. Besides, the evolutionary algorithm is involved to extract the optimal parameters of the deep learning model used in the learning process. This is achieved by using a genetic algorithm that learns the hyper-parameters of the deep architecture. The effectiveness of our methodology has been demonstrated in a series of detailed experiments on a set of Twitter archives. From the results of the experiments, it is clear that the proposed method is superior to the baseline methods in terms of efficiency.publishedVersio

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users\u27 queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy.publishedVersio