Identifying topic relevant hashtags in Twitter streams

Abstract

Hashtags have become a crucial social media tool. The categorization of posts in a simple and informal manner stimulates the dissemination of content through the web. At the same time, it enables users to find messages within a specific topic of their interest. However, the flexibility provided to the user to apply any hashtag carries some problems. Equivalent expressions, like synonyms, are handled like entirely different words, while the same hashtag may refer to distinct topics. Also, many hashtags are dynamic in the sense their meaning and connections with different subjects change through time and location. This factors may hinder content discovery, specially when discussing less popular subjects. One way to overcome this problem is to provide utilities to identify relevant hashtags. Some research in hashtag recommendation in Twitter has been conducted over recent years but with greater focus on proposing hashtags for new posts instead of for a topic in general. Additionally, most of the current approaches rely on databases which require time to be assembled and rigorous maintenance to keep updated.The approach we propose for the identification of topic relevant hashtags is the development of a method to search Twitter, in real time, for hashtags relevant to a topic and represent them in a graph. For this task, we first retrieve tweets within some degrees of connection with the subject. Next, we employ Latent Dirichlet Allocation and Support Vector Machines, to classify tweets and collect their hashtags relevant to the subject. Finally, we use these hashtags to assemble a network of relations that can be used to deepen content retrieval on the original subject. This approach takes into account factors such as the popularity of the hashtags and current meaning.Furthermore, we analyze the proposed algorithm, both qualitatively and quantitatively, and compare it with past approaches, in order to evaluate its performance. The outcomes of our method are usually equal or superior to the available alternatives, in relation to the number of returned hashtags, and current relevance to the topic. However, our process is by default significantly slower than the existing alternatives.

Memahami Persepsi Nilai Utilitas Konsumen dan Dampaknya Terhadap Intensi Penggunaan Hashtag Dalam Kampanye Public Relations

“…Hashtag atau tagar dapat dimaknai sebagai sistem kurasi percakapan publik melalui kata kunci yang digunakan di situs media sosial yang memudahkan untuk menemukan postingan atau informasi dengan tema atau isian konten tertentu (Kim & Hyun, 2019). Hashtag membantu mengkategorisasikan pesan komunikasi dalam konten media sehingga memungkinkan pengguna memahami komunikasi dalam topik tertentu (Figueiredo & Jorge, 2019). Dalam penelitian ini penggunaan hashtag di media sosial merupakan tools atau alat berkomunikasi, dimana Kim dan Hyun (2019) menjelaskan bahwa audiens komunikasi memiliki persepsi nilai tertentu ketika menggunakan media berbasis teknologi infomasi ( Kim & Hyun, 2019).…”

Real-Time Tweet Analytics Using Hybrid Hashtags on Twitter Big Data Streams

“…Information 2020, 11, 341 6 of 23 Some recent studies identify topic-relevant hashtags [10] and predict peak time popularity [8] for tweet hashtags. Research in [10] used latent Dirichlet allocation (LDA) [67] first to determine the topic distributions and then a support vector machine (SVM) to divide the tweets according to their relevance to the topic. Finally, all of the topic distributions are associated with their class labels to identify relevant and irrelevant hashtags.…”

“…Recent research in tweet analytics has studied how hashtags can be effectively applied for determining peak time popularity [8,9], tweet retrieval [10][11][12], and tweet keyword extraction [13]. While many hashtag applications are successful, tweet topic classification using hashtags remains challenging due to the evolving nature of tweets and hashtags.…”

The Relationship between “Zero Waste” and Food: Insights from Social Media Trends

“…Refs. [6][7][8] Twitter is the most popular and active place for food-and environment-oriented communities, compared with, for example, Facebook or YouTube [9]. Hashtags as a tool for sharing certain topic areas are very popular: almost 40% (39.2%) of all tweets contain at least one.…”