Microblogging

About: Microblogging is a research topic. Over the lifetime, 4186 publications have been published within this topic receiving 137030 citations. The topic is also known as: microblog.

The Diffusion of Government Microblogging

Abstract: Governments across many countries are adopting new social media (e.g. twitter), and police departments are engaging in the bandwagon too. We empirically examine the spread of police microblogging in Chinese municipal police departments from the perspective of organizational innovation diffusion. The results show that government size, internet penetration rate, regional diffusion effects and upper-tier pressure are positively and significantly associated with the adoption and earliness of police microblogging, whereas fiscal revenue, economic development and openness, E-government and public safety have no significant effects. We also find that police microblogging diffusion is contingent on different variables at different phases.

Connecting Social Media to E-Commerce: Cold-Start Product Recommendation using Microblogging Information

Abstract: In recent years, the boundaries between ecommerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper we propose a novel solution for cross-site coldstart product recommendation which aims to recommend products from e-commerce websites to users at social networking sites in “cold-start” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users’ social networking features to another feature representation for product recommendation. In specific, we propose learning both users’ and products’ feature representations (called user embeddings and product embeddings, respectively) from data collected from ecommerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users’ social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service SINA WEIBO and the largest Chinese B2C e-commerce website JINGDONG have shown the effectiveness of our proposed framework.

Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore

Abstract: Microblogging has become one of the most useful tools for sharing everyday life events and news and for expressing opinions about those events. As Twitter posts are short and constantly being generated, they are a great source for providing public sentiment towards events that occurred throughout the COVID-19 period in Singapore. In this project, we perform sentiment analysis and topic modeling on the tweets about COVID-19 in Singapore, from 1 February 2020 to 31 August 2020. We accomplished this by collecting tweets discussing about COVID-19 and geolocated as ‘Singapore’, using the Python library ‘SNSCRAPE’. We used the sentiments returned from the VADER lexicon-based classifier and emotions from pre-trained recurrent neural networks to find correlations between real-life events and sentiment changes throughout the whole period. From our analysis, we discovered an increase in tweets about COVID-19 during key periods such as the circuit breaker and found that the overall sentiment polarity was dominantly positive. However, emotion analysis revealed that there were changes in the prevalence of fear and joy emotions over time, due to real-life COVID-19 developments in Singapore. Additionally, sentiment polarity was found to differ from topic to topic.

Emotion tokens: bridging the gap among multilingual twitter sentiment analysis

Abstract: Twitter is a microblogging service where worldwide users publish their feelings. However, sentiment analysis for Twitter messages (tweets) is regarded as a challenging problem because tweets are short and informal. In this paper, we focus on this problem by the analysis of emotion tokens, including emotion symbols (e.g. emoticons), irregular forms of words and combined punctuations. According to our observation on five million tweets, these emotion tokens are commonly used (0.47 emotion tokens per tweet). They directly express one's emotion regardless of his language; hence become a useful signal for sentiment analysis on multilingual tweets. Firstly, emotion tokens are extracted automatically from tweets. Secondly, a graph propagation algorithm is proposed to label the tokens' polarities. Finally, a multilingual sentiment analysis algorithm is introduced. Comparative evaluations are conducted among semantic lexicon based approach and some state-of-the-art Twitter sentiment analysis Web services, both on English and non-English tweets. Experimental results show effectiveness of the proposed algorithms.