Mixed bilingual social media analytics: case study: Live Twitter data

2017 ◽  
Author(s):  
Saurabh Malgaonkar ◽  
Aejazul Khan ◽  
Abhishek Vichare
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Twitter Data

scholarly journals Social Media Analytics: A Case Study of Singapore General Election 2020

2020 ◽  
Author(s):  
Sebastian Zhi Tao Khoo ◽  
Leong Hock Ho ◽  
Ee Hong Lee ◽  
Danston Kheng Boon Goh ◽  
Zehao Zhang ◽  
...  
Keyword(s):  
Social Media ◽  
General Election ◽  
Social Media Analytics

scholarly journals Probabilistic Flood Extent Estimates from Social Media Flood Observations

2016 ◽  
Author(s):  
Tom Brouwer ◽  
Dirk Eilander ◽  
Arnejan van Loenen ◽  
Martijn J. Booij ◽  
Kathelijne M. Wijnberg ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Media ◽  
Uncertain Data ◽  
Added Value ◽  
Hydraulic Models ◽  
Data Errors ◽  
Twitter Data ◽  
Elevation Data ◽  
Flood Maps

Abstract. The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, creates a growing need for accurate and timely flood maps. This research focussed on creating flood maps using user generated content from Twitter. Twitter data has added value over traditional methods such as remote sensing and hydraulic models, since the data is available almost instantly, in contrast to remote sensing and requires less detail than hydraulic models. Deterministic flood maps created using these data showed good performance (F(2) = 0.69) for a case study in York (UK). For York the main source of uncertainty in the probabilistic flood maps was found to be the error of the locations derived from the Twitter data. Errors in the elevation data and parameters of the applied algorithm contributed less to flood extent uncertainty. Although the generated probabilistic maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.

Deep Learning in Social Media Analysis

2021 ◽  
pp. 164-194
Author(s):  
Vaishali Yogesh Baviskar ◽  
Rachna Yogesh Sable
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Media Analysis ◽  
Network Services ◽  
Social Media Analytics ◽  
Learning Approaches ◽  
Election Result ◽  
The Social ◽  
Social Network Services

Social media analytics keep on collecting the information from different media platforms and then calculating the statistical data. Twitter is one of the social network services which has ample amount of data where many users used post significant amounts of data on a regular basis. Handling such a large amount of data using traditional tools and technologies is very complicated. One of the solutions to this problem is the use of machine learning and deep learning approaches. In this chapter, the authors present a case study showing the use of Twitter data for predicting the election result of the political parties.

Application of social media analytics: a case of analyzing online hotel reviews

2017 ◽  
Vol 41 (7) ◽  
pp. 921-935 ◽  
Author(s):  
Wu He ◽  
Xin Tian ◽  
Ran Tao ◽  
Weidong Zhang ◽  
Gongjun Yan ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
Customer Reviews ◽  
Analysis Techniques ◽  
Online Customer Reviews ◽  
Media Data

Purpose Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study. Design/methodology/approach This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews. Findings The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff. Originality/value This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

Measuring and comparing service quality metrics through social media analytics: a case study

2017 ◽  
Vol 16 (3) ◽  
pp. 579-600 ◽  
Author(s):  
Wu He ◽  
Xin Tian ◽  
Andy Hung ◽  
Vasudeva Akula ◽  
Weidong Zhang
Keyword(s):  
Social Media ◽  
Service Quality ◽  
Quality Metrics ◽  
Social Media Analytics

scholarly journals Lupus patients on Twitter: Expressed symptoms and attitudes toward using the platform for healthcare engagement (Preprint)

2019 ◽  
Author(s):  
Alden Bunyan ◽  
Swamy Venuturupalli ◽  
Katja Reuter
Keyword(s):  
Social Media ◽  
Data Extraction ◽  
Analysis Data ◽  
Self Report ◽  
Social Media Analytics ◽  
Translational Science ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Twitter Data ◽  
The U.S

BACKGROUND Lupus is a complex autoimmune disease that is difficult to diagnose and treat. It is estimated that at least 5 million Americans have lupus, with more than 16,000 new cases of lupus being reported annually in the U.S. Social media provides a platform for patients to find rheumatologists, peers, and build awareness of the condition. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. However, there is a lack of research about the characteristics of lupus patients on Twitter and their attitudes toward using Twitter for engaging them with their healthcare. OBJECTIVE This study has two objectives: (1) to conduct a content analysis of Twitter data published by users (in English) in the U.S. between 9/1/2017 and 10/31/2018 to identify patients who publicly discuss their lupus condition and to assess their expressed health themes, and (2) to conduct a cross-sectional survey among these lupus patients on Twitter to study their attitudes toward using Twitter for engaging them with their healthcare. METHODS This is a mixed-methods study that analyzes retrospective Twitter data and conducts a cross-sectional survey among lupus patients on Twitter. We will use Symplur Signals, a healthcare social media analytics platform, to access the Twitter data and analyze user-generated posts that include keywords related to lupus. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among lupus patients. We will further conduct self-report surveys via Twitter by inviting all identified lupus patients who discuss their lupus condition on Twitter. The goal of the survey is to collect data about the characteristics of lupus patients (e.g., gender, race/ethnicity, educational level) and their attitudes toward using Twitter for engaging them with their healthcare. RESULTS This study has been funded by the National Center for Advancing Translational Science (NCATS) through a Clinical and Translational Science Award (CTSA) award. The Institutional Review Board at the University of Southern California (HS-19-00048) approved the study. Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to “lupus” from users in the U.S. published in English between 9/1/2017 and 10/31/2018. We will include 40,885 posts in the analysis. Data analysis will be completed by the end of 2019. CONCLUSIONS The data obtained in this pilot study will shed light on whether Twitter provides a promising data source for garnering health-related attitudes among lupus patients. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of lupus among patients and healthcare providers and implementing related health education interventions. CLINICALTRIAL N/A

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