A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links

Abstract

Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags into links. We observed that the universal properties of the real networks like small-world property, clustering and power-law distributions in various network parameters are also evident in the multilayer network of hashtags. Moreover, we outlined that co-occurrences of hashtags in tweets are mostly coupled with semantic relations, whereas a small number of semantically unrelated, therefore random links reduce node separation and network diameter in the co-occurrence network layer. Together with the degree distributions, the power-law consistencies of degree difference, edge weight and cosine similarity distributions in both layers are also appealing forms of Zipf’s law evident in nature.

Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach

“…In Türker and Sulak (2018) , the authors carried out a study to evaluate the meaningfulness of hashtags within tweets and if the co-occurrence of multiple hashtags is actually linked by a semantic correlation. In fact, it can often happen that, instead of inserting hashtags that reflect the topic discussed in the tweet, the author decides to insert other hashtags completely unrelated to the actual topic.…”

A Deep Learning Model Based on BERT and Sentence Transformer for Semantic Keyphrase Extraction on Big Social Data

“…Twitter users convey their message in 140 characters and can connect with some topics using the shortened or joined words or word groups called hashtags, which start with the "#" character. In this way, their tweets can be listed in a general search of a particular hashtag [32]. We collected tweets related to the following hashtags Tokyo Olympics 2021, National Education Policy, cybercrime, human rights, and covid-19.…”

“…The most significant and effective way is to use pretrained sentence transformers like Generative Pre-trained Transformer (GPT-1) [25], BERT [26], Transformer-XL model pre-trained (XLNet) [27], Robustly Optimized BERT Pre-training Approach (Roberta) [28], Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) [29], Text-to-Text Transfer Transformer (T5) [30], Bidirectional and Auto-Regressive Transformer (BART) [32] to get tweets embedding and then use similarity metric to compute similarity score. Transformer-based pre-trained models have attained remarkable success is mainly due to their ability to learn universal language representation from a massive corpus of unlabeled text data.…”

A survey of community detection methods in multilayer networks

“…The emergence of online social networks has altered millions of web users' behavior so that their interactions with each other produce huge amounts of data on various activities. Facebook and Twitter, as the top-two popular social media in our daily life, have been widely employed for social network analysis in recent years (Alimadadi et al 2019;Türker and Sulak 2018).…”