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.

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.

Abstract: Background: The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. Objective: The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. Methods: We used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. Results: Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. Conclusions: Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

Taking It All In? Visual Attention in Microblog Consumption

Abstract: Microblogging environments such as Twitter present a modality for interacting with information characterized by exposure to information “streams”. In this work, we examine what information in that stream is attended to, and how that attention corresponds to other aspects of microblog consumption and participation. To do this, we measured eye gaze, memory for content, interest ratings, and intended behavior of active Twitter users as they read their tweet steams. Our analyses focus on three sets of alignments: first, whether attention corresponds to other measures of user cognition such as memory (e.g., do people even remember what they attend to?); second, whether attention corresponds to behavior (e.g., are users likely to retweet content that is given the most attention); and third, whether attention corresponds to other attributes of the content and its presentation (e.g., do links attract attention?). We show a positive but imperfect alignment between user attention and other measures of user cognition like memory and interest, and between attention and behaviors like retweeting. To the third alignment, we show that the relationship between attention and attributes of tweets, such as whether it contains a link or is from a friend versus an organization, are complicated and in some cases counterintuitive. We discuss findings in relation to large scale phenomena like information diffusion and also suggest design directions to help maximize user attention in microblog environments

Mining twitter: A source for psychological wisdom of the crowds

Abstract: Over the last few years, microblogging has gained prominence as a form of personal broadcasting media where information and opinion are mixed together without an established order, usually tightly linked with current reality. Location awareness and promptness provide researchers using the Internet with the opportunity to create “psychological landscapes”—that is, to detect differences and changes in voiced (twittered) emotions, cognitions, and behaviors. In our article, we present iScience Maps, a free Web service for researchers, available from http://maps.iscience.deusto.es/ and http://tweetminer.eu/ . Technologically, the service is based on Twitter’s streaming and search application programming interfaces (APIs), accessed through several PHP libraries, and a JavaScript frontend. This service allows researchers to assess via Twitter the effect of specific events in different places as they are happening and to make comparisons between cities, regions, or countries regarding psychological states and their evolution in the course of an event. In a step-by-step example, it is shown how to replicate a study on affective and personality characteristics inferred from first names (Mehrabian & Piercy, Personality and Social Psychology Bulletin, 19, 755–758 1993) by mining Twitter data with iScience Maps.Results from the original study are replicated in both world regions we tested (the western U.S. and the U.K./Ireland); we also discover base rate of names to be a confound that needs to be controlled for in future research.

“I'm here to help”: How companies' microblog responses to consumer problems influence brand perceptions

Abstract: Purpose – Customers have high expectations that company representatives contacted online will go out of their way to be helpful. One type of social media that may be particularly useful as a customer relationship management (CRM) tool is microblogging platforms such as Twitter. The purpose of this paper is to look at the impact of perceived helpfulness of customer representative microblog responses on people's perceptions of brand trust, brand benevolence, brand attitudes and intentions to try or purchase a brand.Design/methodology/approach – A field experiment was conducted to manipulate three variables: type of helpfulness response (empathetic or problem‐solving), amount of helpfulness (less or more helpful), and interface in which responses were viewed (branded, Google, or Twitter).Findings – The interaction between type of helpfulness and amount of helpfulness led to greater perceptions of company trustworthiness and benevolence when there were many problem‐solving responses than when there were few, ...

Time Aware Knowledge Extraction for Microblog Summarization on Twitter

Abstract: Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.