Semantic search for public opinions on urban affairs: A probabilistic topic modeling-based approach

Num-Symbolic Homophonic Social Net-Words

Many excellent studies about social networks and text analyses can be found in the literature, facilitating the rapid development of automated text analysis technology. Due to the lack of natural separators in Chinese, the text numbers and symbols also have their original literal meaning. Thus, combining Chinese characters with numbers and symbols in user-generated content is a challenge for the current analytic approaches and procedures. Therefore, we propose a new hybrid method for detecting blended numeric and symbolic homophony Chinese neologisms (BNShCNs). Interpretation of the words’ actual semantics was performed according to their independence and relative position in context. This study obtained a shortlist using a probability approach from internet-collected user-generated content; subsequently, we evaluated the shortlist by contextualizing word-embedded vectors for BNShCN detection. The experiments show that the proposed method efficiently extracted BNShCNs from user-generated content.

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

The COVID-19 pandemic has created a global health crisis that has affected economies and societies worldwide. During these times of uncertainty and crisis, people have turned to social media platforms as communication tools and primary information sources. Online discourse is conducted under the influence of many different factors, such as background, culture, politics, etc. However, parallel comparative research studies conducted in different countries to identify similarities and differences in online discourse are still scarce. In this study, we combine the crisis lifecycle and opinion leader concepts and use data mining and a set of predefined search terms (coronavirus and COVID-19) to investigate discourse on Twitter (101,271 tweets) and Sina Weibo (92,037 posts). Then, we use a topic modeling technique, Latent Dirichlet Allocation (LDA), to identify the most common issues posted by users and temporal analysis to research the issue's trend. Social Network Analysis (SNA) allows us to discover the opinion leader on the two different platforms. Finally, we find that online discourse reflects the crisis lifecycle according to the stage of COVID-19 in China and the US. Regarding the status of the COVID-19 pandemic, users of Twitter tend to pay more attention to the economic situation while users of Weibo pay more attention to public health. The issues focused on in online discourse have a strong relationship with the development of the crisis in different countries. Additionally, on the Twitter platform many political actors act as opinion leaders, while on the Weibo platform official media and government accounts control the release of information.

Knowledge Discovery of News Text Based on Artificial Intelligence

The explosion of news text and the development of artificial intelligence provide a new opportunity and challenge to provide high-quality media monitoring service. In this article, we propose a semantic analysis approach based on the Latent Dirichlet Allocation (LDA) and Apriori algorithm, and we realize application to improve media monitoring reports by mining large-scale news text. First, we propose to use LDA model to mine news text topic words and reducing news dimensionality. Then, we propose to use Apriori algorithm to discovering the relationship of topic words. Finally, we discovery the relevance of news text topic words and show the intensity and dependency among topic words through drawing. This application can realize to extract the news topics and discover the correlation and dependency among news topics in mass news text. The results show that the method based on LDA and Apriori can help the media monitoring staff to better understand the hidden knowledge in the news text and improve the media analysis report.

Detecting new Chinese words from massive domain texts with word embedding

Textual information retrieval (TIR) is based on the relationship between word units. Traditional word segmentation techniques attempt to discern the word units accurately from texts; however, they are unable to appropriately and efficiently identify all new words. Identification of new words, especially in languages such as Chinese, remains a challenge. In recent years, word embedding methods have used numerical word vectors to retain the semantic and correlated information between words in a corpus. In this article, we propose the word-embedding-based method (WEBM), a novel method that combines word embedding and frequent n-gram string mining for discovering new words from domain corpora. First, we mapped all word units in a domain corpus to a high-dimension word vector space. Second, we used a frequent n-gram word string mining method to identify a set of candidates for new words. We designed a pruning strategy based on the word vectors to quantify the possibility of a word string being a new word, thereby allowing the evaluation of candidates based on the similarity of word units in the same string. In a comparative study, our experimental results revealed that WEBM had a great advantage in detecting new words from massive Chinese corpora.

Discovery of repost patterns by topic analysis in enterprise social networking

Purpose

An increasing number of users are inspired by enterprises to repost social media messages, which greatly contributes to the dissemination of such messages in an online social network. The purpose of this paper is to discover the repost patterns of users regarding enterprise social media messages to help enterprises improve information management abilities for social media.

Design/methodology/approach

This paper proposes a novel method to discover the repost patterns of users in enterprise social networking (ESN) at the macro-level through topic analysis. Specifically, it proposes the message-diversity metric to measure the latent topic diversity degree of the social media messages. Through this technique, the paper analyzes the message-diversity characteristics of the enterprise social media messages and then explores the repost patterns of users.

Findings

The experimental results show that a high repost rate is more prominent for the messages with diverse latent topics, where message-diversity is as high as 0.5.

Practical implications

The findings have great potential in several management areas, such as employing social media marketing, predicting popular messages, helping enterprises strengthen their online presence, and gathering more potential customers.

Originality/value

This study explores how the repost patterns of users in ESN can be determined through general macro-level behavior of users instead of their micro-level processes. The patterns can also lead to a deeper understanding of which contents can drive people to diffuse information. This study gives an important insight into the information behavior of social media users for enterprise management researchers.