18 research outputs found
Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media
Understanding the usage of multiple OSNs (Online Social Networks) has been of
significant research interest as it helps in identifying the unique and
distinguishing trait in each social media platform that contributes to its
continued existence. The comparison between the OSNs is insightful when it is
done based on the representative majority of the users holding active accounts
on all the platforms. In this research, we collected a set of user profiles
holding accounts on both Twitter and Instagram, these platforms being of
prominence among a majority of users. An extensive textual and visual analysis
on the media content posted by these users revealed that both these platforms
are indeed perceived differently at a fundamental level with Instagram engaging
more of the users' heart and Twitter capturing more of their mind. These
differences got reflected in almost every microscopic analysis done upon the
linguistic, topical and visual aspects.Comment: 4 pages, 8 figure
Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram
Instagram is a relatively new form of communication where users can instantly
share their current status by taking pictures and tweaking them using filters.
It has seen a rapid growth in the number of users as well as uploads since it
was launched in October 2010. Inspite of the fact that it is the most popular
photo sharing application, it has attracted relatively less attention from the
web and social media research community. In this paper, we present a
large-scale quantitative analysis on millions of users and pictures we crawled
over 1 month from Instagram. Our analysis reveals several insights on Instagram
which were never studied before: 1) its social network properties are quite
different from other popular social media like Twitter and Flickr, 2) people
typically post once a week, and 3) people like to share their locations with
friends. To the best of our knowledge, this is the first in-depth analysis of
user activities, demographics, social network structure and user-generated
content on Instagram.Comment: 5 page
Data-driven Analysis of Remote Work in China during the COVID-19 Pandemic
This paper leverages online content to investigate teleworking forced due to the COVID-19 pandemic -- using China as a primary case study. Telecommuting has become popular since February 2020 primarily due to the pandemic, and people have been slowly returning to their office from May 2020. This study focuses on two time windows in the year 2020 to calculate the growth of different job sectors. Our results indicate the negative impact of teleworking in manufacturing industry, but shows that information technology-related industries are less affected by working from home. This paper also investigates the impact of COVID-19 on the stock market and discussed what plan of action the policy-makers should take to provide a good economic environment. In addition to the overall economic situation, the psychological situation of employees will affect the development of a given industry. Therefore, misinformation in certain Chinese social media channels is also a concern studied in this paper specifically examining the rumors and their latent topics. We hope that our work will initiate a dialogue and collaboration between scientists, policy makers and government officials to use these lessons and engage effectively for the betterment of society
Tweeting AI: Perceptions of Lay versus Expert Twitterati
In light of the significant public interest in the AI technology and its impacts, in this research we set out to analyze the contours of public discourse and perceptions of AI, as reflected in the social media. We focus on Twitter, and analyze over two million AI related tweets posted by over 40,000 users. In addition to analyzing the macro characteristics of this whole discourse in terms of demographics, sentiment, and topics, we also provide a differential analysis of tweets from experts vs. non-experts, as well as a differential analysis of male vs. female tweeters. We see that (i) by and large the sentiments expressed in the AI discourse are more positive than is par for twitter (ii) that lay public tend to be more positive about AI than expert tweeters and (iii) that women tend to be more positive about AI impacts than men. Analysis of topics discussed also shows interesting differential patterns across experts vs. non-experts and men vs. women. For example, we see that women tend to focus a lot more on the ethical issues surrounding AI. Our analysis provides a far more nuanced picture of the public discourse on AI