A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter

The availability of massive social media data has enabled the prediction of people’s future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based on the transactional features (such as keyword frequency) and the semantic features (such as sentiment) have been proposed to predict the future cascading trends. However, an in-depth understanding of the pros and cons of semantic and transactional models is lacking. This paper conducts a comparative study of both approaches in predicting information diffusion with three mechanisms: retweet cascade, url cascade, and hashtag cascade. Experiments on Twitter data show that the semantic model outperforms the transactional model, if the exterior pattern is less directly observable (i.e. hashtag cascade). When it becomes more directly observable (i.e. retweet and url cascades), the semantic method yet delivers approximate accuracy (i.e. url cascade) or even worse accuracy (i.e. retweet cascade). Further, we demonstrate that the transactional and semantic models are not independent, and the performance gets greatly enhanced when combining both.

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Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

Fieldwork in Religion ◽

10.1558/firn.40610 ◽

2020 ◽

Vol 14 (2) ◽

pp. 140-159

Author(s):

Anthony-Paul Cooper ◽

Emmanuel Awuni Kolog ◽

Erkki Sutinen

Keyword(s):

Machine Learning ◽

Online Community ◽

High Volume ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Social Media Data ◽

Twitter Data ◽

Resource Intensity ◽

Media Data ◽

Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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Learning Topic Map from Large Scale Social Media Data

Companion Proceedings of the Web Conference 2020 ◽

10.1145/3366424.3382088 ◽

2020 ◽

Author(s):

Hui-Kuo Yang

Keyword(s):

Social Media ◽

Large Scale ◽

Social Media Data ◽

Topic Map ◽

Media Data

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Semantic-Aware Visual Abstraction of Large-Scale Social Media Data With Geo-Tags

IEEE Access ◽

10.1109/access.2019.2935471 ◽

2019 ◽

Vol 7 ◽

pp. 114851-114861 ◽

Cited By ~ 1

Author(s):

Zhiguang Zhou ◽

Xinlong Zhang ◽

Xiaoyun Zhou ◽

Yuhua Liu

Keyword(s):

Social Media ◽

Large Scale ◽

Social Media Data ◽

Media Data

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What Your Tweets Tell Us About You: Identity, Ownership and Privacy of Twitter Data

International Journal of Digital Curation ◽

10.2218/ijdc.v7i1.224 ◽

2012 ◽

Vol 7 (1) ◽

pp. 174-197 ◽

Cited By ~ 9

Author(s):

Heather Small ◽

Kristine Kasianovitz ◽

Ronald Blanford ◽

Ina Celaya

Keyword(s):

Social Media ◽

Social Networking Sites ◽

Data Sets ◽

Data Set ◽

Social Media Data ◽

Twitter Data ◽

Other Information ◽

Rich Data ◽

Additional Value ◽

Media Data

Social networking sites and other social media have enabled new forms of collaborative communication and participation for users, and created additional value as rich data sets for research. Research based on accessing, mining, and analyzing social media data has risen steadily over the last several years and is increasingly multidisciplinary; researchers from the social sciences, humanities, computer science and other domains have used social media data as the basis of their studies. The broad use of this form of data has implications for how curators address preservation, access and reuse for an audience with divergent disciplinary norms related to privacy, ownership, authenticity and reliability.In this paper, we explore how the characteristics of the Twitter platform, coupled with an ambiguous and evolving understanding of privacy in networked communication, and divergent disciplinary understandings of the resulting data, combine to create complex issues for curators trying to ensure broad-based and ethical reuse of Twitter data. We provide a case study of a specific data set to illustrate how data curators can engage with the topics and questions raised in the paper. While some initial suggestions are offered to librarians and other information professionals who are beginning to receive social media data from researchers, our larger goal is to stimulate discussion and prompt additional research on the curation and preservation of social media data.

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Changing Trends in Long-Term Sentiments and Neighborhood Determinants in a Shrinking City

Journal of Planning Education and Research ◽

10.1177/0739456x211044215 ◽

2021 ◽

pp. 0739456X2110442

Author(s):

Yunmi Park ◽

Minju Kim ◽

Jiyeon Shin ◽

Megan E. Heim LaFrombois

Keyword(s):

Social Media ◽

Built Environment ◽

Urban Regeneration ◽

Neighborhood Conditions ◽

Social Media Data ◽

Twitter Data ◽

Changing Trends ◽

Shrinking City ◽

Media Data

This research examined social media’s role in understanding perceptions about the spaces in which individuals interact, what planners can learn from social media data, and how to use social media to inform urban regeneration efforts. Using Twitter data from 2010 to 2018 recorded in one U.S. shrinking city, Detroit, Michigan, this paper longitudinally investigated topics that people discuss, their emotions, and neighborhood conditions associated with these topics and sentiments. Findings demonstrate that neighborhood demographics, socioeconomic, and built environment conditions impact people’s sentiments.

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Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

Social Science Computer Review ◽

10.1177/0894439318822007 ◽

2019 ◽

Vol 38 (5) ◽

pp. 633-650 ◽

Cited By ~ 2

Author(s):

Josh Pasek ◽

Colleen A. McClain ◽

Frank Newport ◽

Stephanie Marken

Keyword(s):

Social Media ◽

Survey Data ◽

Large Scale ◽

Presidential Approval ◽

Social Phenomena ◽

Social Media Data ◽

Complex Picture ◽

Large Scale Survey ◽

Media Data

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

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Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data

Volume 2B: 33rd Computers and Information in Engineering Conference ◽

10.1115/detc2013-12661 ◽

2013 ◽

Cited By ~ 20

Author(s):

Suppawong Tuarob ◽

Conrad S. Tucker

Keyword(s):

Social Media ◽

Knowledge Discovery ◽

Large Scale ◽

Product Information ◽

Product Market ◽

Knowledge Discovery In Databases ◽

Product Reviews ◽

Product Model ◽

Social Media Data ◽

Media Data

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter® and Facebook®, have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

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