scholarly journals Predicting the 2020 US Presidential Election with Twitter

2021 ◽  
Author(s):  
Michael Caballero
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Structural Data ◽  
Accurate Method ◽  
Special Focus ◽  
Campaign Strategies ◽  
Social Media Data ◽  
Us Presidential Election ◽  
Twitter Users ◽  
Media Data

One major sub-domain in the subject of polling public opinion with social media data is electoral prediction. Electoral prediction utilizing social media data potentially would significantly affect campaign strategies, complementing traditional polling methods and providing cheaper polling in real-time. First, this paper explores past successful methods from research for analysis and prediction of the 2020 US Presidential Election using Twitter data. Then, this research proposes a new method for electoral prediction which combines sentiment, from NLP on the text of tweets, and structural data with aggregate polling, a time series analysis, and a special focus on Twitter users critical to the election. Though this method performed worse than its baseline of polling predictions, it is inconclusive whether this is an accurate method for predicting elections due to scarcity of data. More research and more data are needed to accurately measure this method’s overall effectiveness.

scholarly journals Assessing Trust Versus Reliance for Technology Platforms by Systematic Literature Review

2020 ◽  
Vol 6 (2) ◽  
pp. 205630512091388
Author(s):  
Trevor Deley ◽  
Elizabeth Dubois
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Presidential Election ◽  
Success Rates ◽  
Human Relationships ◽  
Social Media Data ◽  
Technology Platforms ◽  
Literature Reviews ◽  
Us Presidential Election ◽  
Media Data

We do not trust technologies like we trust people, rather we rely on them. This article argues for an emphasis on reliance rather than trust as a concept for understanding human relationships with technology. Reliance is important because researchers can empirically measure the reliability of a given technology. We first explore two frameworks of trust and reliance. We then examine how reliance can be measured by conducting systematic literature reviews of reported success metrics for given technologies. Specifically, we examine papers which present models for predicting private traits from social media data. Of the 72 models for predicting private traits that were surveyed from 31 papers, 80% of the methods reported success rates lower than 90%, indicating a general unreliability in predicting private traits. We illustrate the current applicability of this method throughout the article by discussing the Cambridge Analytica scandal that began during the 2016 US Presidential election.

scholarly journals Accurate, Fast, But Not Always Cheap: Evaluating “Crowdcoding” as an Alternative Approach to Analyze Social Media Data

2019 ◽  
Vol 97 (3) ◽  
pp. 811-834 ◽  
Author(s):  
Lei Guo ◽  
Kate Mays ◽  
Sha Lai ◽  
Mona Jalal ◽  
Prakash Ishwar ◽  
...  
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Mass Communication ◽  
The Internet ◽  
Communication Research ◽  
Social Media Data ◽  
Quantitative Content ◽  
Alternative Approach ◽  
Media Data ◽  
Numerous People

Crowdcoding, a method that outsources “coding” tasks to numerous people on the internet, has emerged as a popular approach for annotating texts and visuals. However, the performance of this approach for analyzing social media data in the context of journalism and mass communication research has not been systematically assessed. This study evaluated the validity and efficiency of crowdcoding based on the analysis of 4,000 tweets about the 2016 U.S. presidential election. The results show that compared with the traditional quantitative content analysis, crowdcoding yielded comparably valid results and was superior in efficiency, but was more expensive under most circumstances.

scholarly journals Analyzing Political Polarization on Social Media by Deleting Bot Spamming

2022 ◽  
Vol 6 (1) ◽  
pp. 3
Author(s):  
Riccardo Cantini ◽  
Fabrizio Marozzo ◽  
Domenico Talia ◽  
Paolo Trunfio
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Opinion Mining ◽  
Political Support ◽  
Political Polarization ◽  
Political Issues ◽  
Political Factions ◽  
Us Presidential Election ◽  
Twitter Users ◽  
Rich Data

Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users.

scholarly journals Long-term patient-reported symptoms of COVID-19: an analysis of social media data

2020 ◽  
Author(s):  
Juan M. Banda ◽  
Gurdas Viguruji Singh ◽  
Osaid Alser ◽  
DANIEL PRIETO-ALHAMBRA
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Social Media Data ◽  
Persistent Symptoms ◽  
Patient Reported ◽  
Twitter Users ◽  
Initial Recovery ◽  
Clinical Records ◽  
Media Data

As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent symptoms after confirmed infections on patients even after three months of initial recovery. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually not publicly available or standardized to perform longitudinal analyses on them. Therefore, there is a need to use additional data sources for continued follow-up and identification of latent symptoms that might be underreported in other places. In this work we present a preliminary characterization of post-COVID-19 symptoms using social media data from Twitter. We use a combination of natural language processing and clinician reviews to identify long term self-reported symptoms on a set of Twitter users.

scholarly journals Investigation of Geographic and Macrolevel Variations in LGBTQ Patient Experiences: Longitudinal Social Media Analysis

10.2196/17087 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e17087
Author(s):  
Yulin Hswen ◽  
Amanda Zhang ◽  
Kara C Sewalk ◽  
Gaurav Tuli ◽  
John S Brownstein ◽  
...  
Keyword(s):  
Social Media ◽  
Patient Experience ◽  
State Level ◽  
The United States ◽  
Media Analysis ◽  
Political Climate ◽  
Social Media Data ◽  
Political Identification ◽  
Twitter Users ◽  
Media Data

Background Discrimination in the health care system contributes to worse health outcomes among lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients. Objective The aim of this study is to examine disparities in patient experience among LGBTQ persons using social media data. Methods We collected patient experience data from Twitter from February 2013 to February 2017 in the United States. We compared the sentiment of patient experience tweets between Twitter users who self-identified as LGBTQ and non-LGBTQ. The effect of state-level partisan identity on patient experience sentiment and differences between LGBTQ users and non-LGBTQ users were analyzed. Results We observed lower (more negative) patient experience sentiment among 13,689 LGBTQ users compared to 1,362,395 non-LGBTQ users. Increasing state-level liberal political identification was associated with higher patient experience sentiment among all users but had stronger effects for LGBTQ users. Conclusions Our findings highlight that social media data can yield insights about patient experience for LGBTQ persons and suggest that a state-level sociopolitical environment influences patient experience for this group. Efforts are needed to reduce disparities in patient care for LGBTQ persons while taking into context the effect of the political climate on these inequities.

A mixed-method approach for modelling customer-centric mobile phone reverse logistics: application of social media data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sajjad Shokouhyar ◽  
Amirhossein Dehkhodaei ◽  
Bahar Amiri
Keyword(s):  
Social Media ◽  
Big Data ◽  
Mobile Phone ◽  
Reverse Logistics ◽  
Significant Feature ◽  
Special Focus ◽  
Social Media Data ◽  
Content Type ◽  
Website Analysis ◽  
Media Data

Purpose Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantage and high efficiency by customers because of the expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm’s success. Therefore, paying attention to customer feedback in supply chain and logistics processes has recently increased so that manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumer purchasing behaviour and to analyse the interrelationship among them to develop a framework for customer-centric RL. These features are studied based on website analysis of several mobile phone manufacturers. The special focus of this paper is on social media data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach. Design/methodology/approach A portfolio of mobile phone features that affect consumer’s mobile phone purchasing decisions has been taken from website analysis by several mobile phone manufacturers to achieve this objective. Then, interrelationships between the identified features have been established by using big data supplemented with interpretive structural modelling (ISM). Apart from that, cross-impact matrix multiplication, applied to classification analysis, was carried out to graphically represent these features based on their driving power and dependence. Findings During the study, it has been observed from the ISM that the chip (F5) is the most significant feature that affects customer’s buying behaviour; therefore, mobile phone manufacturers realize that this is to be addressed first. Originality/value The focus of this paper is on social media data (Twitter) so that experts can understand the interaction between mobile phone features that affect consumer’s decisions on mobile phone purchasing by using the results.

Daily and hourly mood pattern discovery of Turkish twitter users

2015 ◽  
Vol 5 (2) ◽  
pp. 90
Author(s):  
Mete Celik ◽  
Ahmet Sakir Dokuz
Keyword(s):  
Social Media ◽  
Big Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Social Media Data ◽  
Web Technologies ◽  
Document Frequency ◽  
Social Media Text ◽  
Twitter Users ◽  
Media Data

<p>Massive amount of data-related applications and widespread usage of web technologies has started big data era. Social media data is one of the big data sources. Mining social media data provides useful insights for companies and organizations for developing their services, products or organizations. This study aims to analyze Turkish Twitter users based on daily and hourly social media sharings. By this way, daily and hourly mood patterns of Turkish social media users could be revealed in positive or negative manner. For this purpose, Support Vector Machines (SVM) classification algorithm and Term Frequency – Inverse Document Frequency (TF-IDF) feature selection technique was used. As far as our knowledge, this is the first attempt to analyze people’s all sharings on social media and generate results for temporal-based indicators like macro and micro levels.</p><p> </p><p>Keywords: big data, social media, text classification, svm, tf-idf term weighting, daily and hourly mood patterns.</p>

scholarly journals Mining Public Opinion about Economic Issues

2018 ◽  
Vol 9 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Amir Karami ◽  
London S. Bennett ◽  
Xiaoyun He
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Text Mining ◽  
Opinion Mining ◽  
Social Media Data ◽  
Economic Issues ◽  
Election Prediction ◽  
Us Presidential Election ◽  
Mining Methods ◽  
Media Data

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.

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