Social and Web Analytics: An Analytical Case Study on Twitter Data

2022 ◽  
pp. 135-143
Author(s):  
Hitesh Kumar Sharma ◽  
Tanupriya Choudhury ◽  
Hussain Falih Mahdi
Keyword(s):  
Web Analytics ◽  
Twitter Data

scholarly journals Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Harisu Abdullahi Shehu ◽  
Md. Haidar Sharif ◽  
Md. Haris Uddin Sharif ◽  
Ripon Datta ◽  
Sezai Tokat ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Twitter Data

scholarly journals Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques

2021 ◽  
Vol 11 (22) ◽  
pp. 10596
Author(s):  
Chung-Hong Lee ◽  
Hsin-Chang Yang ◽  
Yenming J. Chen ◽  
Yung-Lin Chuang
Keyword(s):  
Real Time ◽  
Data Science ◽  
Clustering Algorithm ◽  
Language Models ◽  
Event Monitoring ◽  
Data Set ◽  
Intelligence Gathering ◽  
Twitter Data ◽  
The Government

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

scholarly journals A METRICS BASED APPROACH TO ANALYSE WEB USAGE PATTERN

2011 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Sukhjinder Singh ◽  
Amit Makkar ◽  
Navneet Singh
Keyword(s):  
Customer Behavior ◽  
Usage Pattern ◽  
Web Analytics ◽  
Business Objectives ◽  
Web Usage ◽  
Use Of Data ◽  
Web Metrics

Web metrics are established goals and standards for measuring website performance. Web Analytics can be used to analyze and statistically process user and customer behavior. Web Analytics especially refers to the use of data collected from a Website to determine which aspects of the Website work towards the business objectives. This paper provides a web metrics based approach that can be used to analyze and improve web usage pattern. We define a set of 15 web metrics that can play an important role in understanding web visitors behavior and provide suggestion how these metrics can help in making a website more popular. We describe the approach by considering a case study of the website gndu.ac.in for the data collected over a period of five months.

scholarly journals Twitter Diplomacy @Kemlu_RI: A Case Study of Bali Democracy Forum 2019

2021 ◽  
Vol 10 (1) ◽  
pp. 31-43
Author(s):  
Ludiro Madu
Keyword(s):  
Foreign Affairs ◽  
Online Media ◽  
Network Analyses ◽  
Twitter Data ◽  
Twitter Account ◽  
Dea Analysis ◽  
Use Of Social Media ◽  
Digital Diplomacy ◽  
Social Network Analyses

Following the increasing use of social media, particularly Twitter, the Indonesian MOFA made an official Twitter account @Kemlu_RI for disseminating information. The paper aims to to analyse the trend of tweeting at the official Twitter account of the Indonesian Ministry of Foreign Affairs (MOFA), @Kemlu_RI. This research used a qualitative approach through online media, Twitter. Data was obtained through observation of the conversation trend on Twitter by monitoring @Kemlu_RI account. This research focussed on the use of hashtag #BDF2019. Using ‘Drone Emprit Academic’ (DEA), this research collected data on five days of Twitter conversation. The DEA analysis resulted in 1.088 conversations. Rather than only disseminating information, the use of DEA for analysing #BDF2019 at @Kemlu_RI turned out to produce more findings. The result of this study shows that the DEA usage gives more benefits to Indonesian digital diplomacy, such as top hashtag, top influencer, social network analyses, and most engaged users, rather than just general dissemination of information of the BDF 2019. Therefore, the use of the DEA is very significant for resulting in more accurate data for Indonesian MOFA in order to manage a better strategy for using Twitter in its future diplomatic agenda.

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.

scholarly journals A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

2014 ◽  
Vol 16 (10) ◽  
pp. e236 ◽  
Author(s):  
Ruchit Nagar ◽  
Qingyu Yuan ◽  
Clark C Freifeld ◽  
Mauricio Santillana ◽  
Aaron Nojima ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Influenza Season ◽  
Twitter Data

Lack of Alignment in Emergency Response by Systems and the Public: A Dutch Disaster Health Literacy Case Study

2020 ◽  
pp. 1-4
Author(s):  
Kristine Sørensen
Keyword(s):  
Public Health ◽  
Health Literacy ◽  
Emergency Response ◽  
Disaster Response ◽  
Safety Information ◽  
Health And Safety ◽  
The Public ◽  
Twitter Data ◽  
Areas Of Concern

ABSTRACT Objective: Disaster health literacy is vital for emergency medicine and public health preparedness. Conversely, how health and safety information is communicated has a significant impact on disaster health literacy. A lack of alignment between the disaster response and the public’s reaction was apparent during a Dutch chemical incident. This case study aims to provide insights into why that misalignment occurred. Methods: The case study used readily available Twitter data. The tweets represented both the public and the authorities. The tweets were coded, thematically categorised, analysed, and synthesised to generate an explanatory framework describing the obstacles experienced during the emergency. Results: The analysis identified four areas of concern with regards to the lack of alignment between the authorities and the public: the alert of the chemical incident, the inadequate communication, the problematic disaster management, and the insufficient disaster health literacy. Conclusion: The case study showed shortcomings in communication and a lack of alignment in the emergency response of the authorities as well as the public’s disaster health literacy. Immediate action points were apparent, and a more profound evaluation is recommended to avoid further escalation of an emergency in the future. Trust needs to be built before the next incident strikes.

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