scholarly journals Public Reaction to Scientific Research via Twitter Sentiment Prediction

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Murtuza Shahzad ◽  
Hamed Alhoori
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Prediction Models ◽  
Binary Classification ◽  
Computational Social Science ◽  
Research Articles ◽  
Emotional Impact ◽  
Scientific Publications ◽  
The Public ◽  
The Impact

Abstract Purpose Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles. Design/methodology/approach Several tools are used for sentiment analysis, so we applied five sentiment analysis tools to check which are suitable for capturing a tweet's sentiment value and decided to use NLTK VADER and TextBlob. We segregated the sentiment value into negative, positive, and neutral. We measure the mean and median of tweets’ sentiment value for research articles with more than one tweet. We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models. Findings We found that the most important feature in all the models was the sentiment of the research article title followed by the author count. We observed that the tree-based models performed better than other classification models, with Random Forest achieving 89% accuracy for binary classification and 73% accuracy for three-label classification. Research limitations In this research, we used state-of-the-art sentiment analysis libraries. However, these libraries might vary at times in their sentiment prediction behavior. Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper's details. In the future, we intend to broaden the scope of our research by employing word2vec models. Practical implications Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes. Research in this area has relied on fewer and more limited measures, such as citations and user studies with small datasets. There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research. This study will help scientists better comprehend the emotional impact of their work. Additionally, the value of understanding the public's interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public. Originality/value This study will extend work on public engagement with science, sociology of science, and computational social science. It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.

scholarly journals A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

2020 ◽  
Vol 4 (4) ◽  
pp. 33
Author(s):  
Toni Pano ◽  
Rasha Kashef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Prediction Models ◽  
Financial Sector ◽  
Research Gap ◽  
Text Preprocessing ◽  
The Impact

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

Sentiment Analysis as a Tool to Understand the Cultural Relationship between Consumer and Brand

2017 ◽  
pp. 202-215 ◽  
Author(s):  
Nicola Capolupo ◽  
Gianpaolo Basile ◽  
Giancarlo Scozzese
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Public Opinion ◽  
Web 2.0 ◽  
Sentiment Analysis ◽  
Product Launch ◽  
Consumer Sentiment ◽  
Specific Product ◽  
The Public ◽  
The Impact

One of the most relevant issues that companies, offices and marketing experts, sociologists and scholars must address studying a new brand or product launch is without any doubt the impact - in terms of feedback - on the consumer sentiment. The study of users' opinions on a specific product or brand has changed with the advent of Web 2.0, which has overcome the old surveys model leading consumers in a too complex and not genuine area, reaching more sophisticated research or even better tracking their opinions directly “on the field”, i.e. in the community where this exchange of views and information happens naturally and not artificially. The analysis of consumers' opinions on social media provides enormous opportunities for the public and the private spheres. Concerning the last on the reputation of a certain product/brand or firm is strongly influenced by the voices and negative opinions published and shared by users on social networks. Indeed, companies need to adapt their behaviour monitoring public opinion.

#INCLUSION – THE IMPACT OF SOCIAL MEDIA COMMUNICATION ON THE LEGITIMACY OF ARMED FORCES

2019 ◽  
pp. 43-60
Author(s):  
EVA MOEHLECKE DE BASEGGIO ◽  
OLIVIA SCHNEIDER ◽  
TIBOR SZVIRCSEV TRESCH
Keyword(s):  
Social Media ◽  
Political Communication ◽  
Structural Equation ◽  
Armed Forces ◽  
Equation Model ◽  
Media Communication ◽  
The Public ◽  
Political Legitimation ◽  
Social Media Communication ◽  
The Impact

The Swiss Armed Forces (SAF), as part of a democratic system, depends on legitimacy. Democracy, legitimacy and the public are closely connected. In the public sphere the SAF need to be visible; it is where they are controlled and legitimated by the citizens, as part of a deliberative discussion in which political decisions are communicatively negotiated. Considering this, the meaning of political communication, including the SAF’s communication, becomes obvious as it forms the most important basis for political legitimation processes. Social media provide a new way for the SAF to communicate and interact directly with the population. The SAF’s social media communication potentially brings it closer to the people and engages them in a dialogue. The SAF can become more transparent and social media communication may increase its reputation and legitimacy. To measure the effects of social media communication, a survey of the Swiss internet population was conducted. Based on this data, a structural equation model was defined, the effects of which substantiate the assumption that the SAF benefits from being on social media in terms of broadening its reach and increasing legitimacy values.

New Wars and Their Visual Representation: Dead Bodies without Graves/Mourne

2020 ◽  
Vol 2 ◽  
pp. 1-18
Author(s):  
Tuncay Şur ◽  
Betül Yarar
Keyword(s):  
Social Media ◽  
Visual Representation ◽  
Human Beings ◽  
The Public ◽  
Photographic Images ◽  
War Photography ◽  
New Wars ◽  
The Social ◽  
History Of ◽  
The Impact

This paper seeks to understand why there has been an increase in photographic images exposing military violence or displaying bodies killed by military forces and how they can freely circulate in the public without being censored or kept hidden. In other words, it aims to analyze this particular issue as a symptom of the emergence of new wars and a new regime of their visual representation. Within this framework, it attempts to relate two kinds of literature that are namely the history of war and war photography with the bridge of theoretical discussions on the real, its photographic representation, power, and violence.  Rather than systematic empirical analysis, the paper is based on a theoretical attempt which is reflected on some socio-political observations in the Middle East where there has been ongoing wars or new wars. The core discussion of the paper is supported by a brief analysis of some illustrative photographic images that are served through the social media under the circumstances of war for instance in Turkey between Turkish military troops and the Kurdish militants. The paper concludes that in line with the process of dissolution/transformation of the old nation-state formations and globalization, the mechanism and mode of power have also transformed to the extent that it resulted in the emergence of new wars. This is one dynamic that we need to recognize in relation to the above-mentioned question, the other is the impact of social media in not only delivering but also receiving war photographies. Today these changes have led the emergence of new machinery of power in which the old modern visual/photographic techniques of representing wars without human beings, torture, and violence through censorship began to be employed alongside medieval power techniques of a visual exhibition of tortures and violence.

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

Correcting Misperceptions About Genetically Modified Food on Social Media: Examining the Impact of Experts, Social Media Heuristics, and the Gateway Belief Model

2020 ◽  
pp. 107554702098137
Author(s):  
Leticia Bode ◽  
Emily K. Vraga ◽  
Melissa Tully
Keyword(s):  
Social Media ◽  
Food Safety ◽  
Genetically Modified ◽  
Human Consumption ◽  
Genetically Modified Food ◽  
The Public ◽  
Scientific Consensus ◽  
Gm Food ◽  
Belief Model ◽  
The Impact

We experimentally test whether expert organizations on social media can correct misperceptions of the scientific consensus on the safety of genetically modified (GM) food for human consumption, as well as what role social media cues, in the form of “likes,” play in that process. We find expert organizations highlighting scientific consensus on GM food safety reduces consensus misperceptions among the public, leading to lower GM misperceptions and boosting related consumption behaviors in line with the gateway belief model. Expert organizations’ credibility may increase as a result of correction, but popularity cues do not seem to affect misperceptions or credibility.

scholarly journals Determinants of Social Media Impact in Local Government

2016 ◽  
Vol 28 (3) ◽  
pp. 82-103 ◽  
Author(s):  
Mohd Hisham Mohd Sharif ◽  
Indrit Troshani ◽  
Robyn Davidson
Keyword(s):  
Social Media ◽  
Local Government ◽  
Structural Equation ◽  
Limited Attention ◽  
Security Risks ◽  
Media Impact ◽  
The Public ◽  
Perceived Security ◽  
The Impact ◽  
Media Data

Limited attention has been directed towards understanding the impact of social media in the public sector, particularly in local government organisations. Although social media offer substantial benefits and opportunities to local government, research into the impact of social media remains scant. To address this gap, the authors draw on the technology, organisation, and environment (TOE) framework and propose a model of the determinants of social media impact in local government. The model is tested with data collected via a survey with 173 Australian local government organisations using social media. Data were analysed using the partial least squares-structural equation modelling (PLS-SEM) technique. The results indicate that TOE factors including perceived benefits, perceived security risks, compatibility, and degree of formalisation are important predictors of social media impact in local government.

scholarly journals Social media and sentiment in bioenergy consultation

2016 ◽  
Vol 10 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Victoria Uren ◽  
Daniel Wright ◽  
James Scott ◽  
Yulan He ◽  
Hassan Saif
Keyword(s):  
Social Media ◽  
New Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Public Consultation ◽  
Short Paper ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aasif Ahmad Mir ◽  
Sevukan Rathinam ◽  
Sumeer Gul
Keyword(s):  
Social Media ◽  
Public Perception ◽  
English Language ◽  
Social Issues ◽  
Application Programming Interface ◽  
Content Type ◽  
The Public ◽  
Public Reaction ◽  
Types Of Information ◽  
The Impact

PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

Public relations in the age of data: corporate perspectives on social media analytics (SMA)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kathy R. Fitzpatrick ◽  
Paula L. Weissman
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Social Media Analytics ◽  
Content Type ◽  
The Public ◽  
Management Function ◽  
Use Of Social Media ◽  
The Impact ◽  
Corporate Brands ◽  
Depth Interviews

PurposeThe aim of this study was to understand how public relations leaders view and use social media analytics (SMA) and the impact of SMA on the public relations function.Design/methodology/approachThe research involved in-depth interviews with chief communication officers (CCOs) from leading multinational corporate brands.FindingsThe findings revealed that although CCOs perceive social media analytics as strategically important to the advancement of public relations, the use of social media data is slowed by challenges associated with building SMA capacity.Theoretical and practical implications – The research extends public relations theory on public relations as a strategic management function and provides practical insights for building SMA capabilities.Originality/valueThe study is among the first to provide empirical evidence of how companies are using social media analytics to enhance public relations efforts.

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