Location-based Sentiment Analyses and Visualization of Twitter Election Data

Yaqub, Ussama; Sharma, Nitesh; Pabreja, Rachit; Chun, Soon Ae; Atluri, Vijayalakshmi; Vaidya, Jaideep

doi:10.1145/3339909

Cited by 33 publications

(35 citation statements)

References 32 publications

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“… Engineering ○ ● Yao et al. (2015) IEEE Transactions on Information Forensics and Security Computer Networks and Communications ○ Yaqub et al. (2020) Digital Government: Research and Practice Information systems, Law ○ ● Yu et al.…”

Section: Methodology Developmentmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

162

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Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users’ mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users’ mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.

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Section: Methodology Developmentmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

162

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“…As their exploration showed, the online sentiment in many ways reflected real public opinion. They postulate that being able to understand the online (Twitter) sentiment can greatly predict the public opinion or decision that is forthcoming regardless of differences in data collection [20]. A larger tweet dataset and inclusive study of all states (US), not just ten populous states, would be more beneficial to determine the outliers.…”

Section: Carlos Costa Et Al To Address Their Explanatory Research Obj...mentioning

confidence: 99%

Sentiment Analysis of Cyber Security Content on Twitter and Reddit

Thapa¹

2022

Data Mining and Machine Learning

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Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.

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“…For example rise in use of Twitter for election campaigning has greatly expanded research in the area of election prediction through data analytic (Tumasjan et al, 2010;Shi et al, 2012). Studies have also looked at the potential of utilizing Twitter location feature to gauge candidate support not only at the national level but also at regional and state level (Yaqub et al, 2020).…”

Section: Previous Researchmentioning

confidence: 99%

Analyzing social media messages of public sector organizations utilizing sentiment analysis and topic modeling

Yaqub

Chun

Atluri

et al. 2021

Self Cite

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In this paper, we perform sentiment analysis and topic modeling on Twitter and Facebook posts of nine public sector organizations operating in Northeast US. The study objective is to compare and contrast message sentiment, content and topics of discussion on social media. We discover that sentiment and frequency of messages on social media is indeed affected by nature of organization’s operations. We also discover that organizations either use Twitter for broadcasting or one-to-one communication with public. Finally we found discussion topics of organizations – identified through unsupervised machine learning – that engaged in similar areas of public service having similar topics and keywords in their public messages. Our analysis also indicates missed opportunities by these organizations when communication with public. Findings from this study can be used by public sector entities to understand and improve their social media engagement with citizens.

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Location-based Sentiment Analyses and Visualization of Twitter Election Data

Cited by 33 publications

References 32 publications

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Sentiment Analysis of Cyber Security Content on Twitter and Reddit

Analyzing social media messages of public sector organizations utilizing sentiment analysis and topic modeling

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