Predicting literature’s early impact with sentiment analysis in Twitter

Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. ‘number of unique Twitter users’, improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature’s early impact

Artificial Intelligence Adoption in Criminal Incestigations: Challenges and Opportunities for Research

Artificial Intelligence (AI) offers the potential to transform organisational decision-making and knowledge-sharing processes that support criminal investigations. Yet, there is still limited evidence-based knowledge concerning the successful use of AI for criminal investigations in literature. This paper identifies the main areas and current dynamics of the adoption of AI in criminal investigations using bibliometric analysis. We synthesise existing research by identifying key themes researchers have delved into on AI in criminal investigations. The themes include crime prediction and human-centred issues relating to AI use in criminal investigations. Finally, the paper elaborates on the challenges that may influence AI adoption in criminal investigations by police professionals. These challenges include possible laggard effects with AI adoption, implementation challenges, lack of government oversight, and a skills gap

Exploiting tweet sentiments in altmetrics large-scale data

This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users’ sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialised lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarisation approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users’ expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business and Decision Sciences, tweet aspects are focused on the results section. In contrast, in Physics and Astronomy, Materials Sciences and Computer Science, these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact

Temporal pattern mining from user generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content; however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available; the link is given in the dataset section

Fake News Detection in Social Media Using Machine Learning and Deep Learning

Fake news detection in social media is a process of detecting false information that is intentionally created to mislead readers. The spread of fake news may cause social, economic, and political turmoil if their proliferation is not prevented. However, fake news detection using machine learning faces many challenges. Datasets of fake news are usually unstructured and noisy. Fake news often mimics true news. In this study, a data preprocessing method is proposed for mitigating missing values in the datasets to enhance fake news detection accuracy. The experimental results show that Multi- Layer Perceptron (MLP) classifier combined with the proposed data preprocessing method outperforms the state-of-the-art methods. Furthermore, to improve the early detection of rumors in social media, a time-series model is proposed for fake news detection in social media using Twitter data. With the proposed model, computational complexity has been reduced significantly in terms of machine learning models training and testing times while achieving similar results as state-of-the-art in the literature. Besides, the proposed method has a simplified feature extraction process, because only the temporal features of the Twitter data are used. Moreover, deep learning techniques are also applied to fake news detection. Experimental results demonstrate that deep learning methods outperformed traditional machine learning models. Specifically, the ensemble-based deep learning classification model achieved top performance

Sentiment effects in professionally traded markets: evidence from oil and emissions futures

This thesis shows that sentiment has influence in professionally traded oil and emissions markets. The sentiment index of Baker and Wurgler (2006) is adapted for the oil markets and is used to show that sentiment has a positive effect on WTI and Brent crude oil prices. Having established the value of this index in the oil markets it is extended to include the wider energy markets and used to show that sentiment also has an effect in the EU emissions trading scheme (EU ETS). It is found that there is some evidence that decisions of the European Parliament (EP) are associated with a drop in emission allowance (EUA) prices particularly when these decisions occur at times of low sentiment, low news exposure and when they come from non-party political sources. It is found that an increase in volatility of EUA returns is associated with EP decisions made at these times. In order to investigate further the effect of sentiment in the EU ETS, sentiment measured from tweets concerning the emissions market is shown to predict price level and volatility using intra-day data. Bi-directional Granger causality is found between changes in emissions market sentiment and EUA returns, this is especially true for negative sentiment. There is only very weak evidence of an association between climate change sentiment and the EUA returns showing that the EU ETS is not very high in the consciousness of people posting tweets about climate change. Finally, there is some evidence that energy commodity prices and stock market returns can explain, but not predict, EUA prices. This suggests that the EU ETS is efficient with regard to this fundamental information but that in general the Efficient Market Hypothesis does not provide a complete description of the market dynamics. This thesis therefore shows not only that the Efficient Market Hypothesis does not provide a complete description of market dynamics but that sentiment does not rely on uninformed traders to have a real and substantial effect in the emissions and oil markets

Armed actors and the fallacy of the state: non-state dichotomy: State-mediated networks of coercive power in Mindanao

C

The appraisal of Facebook online community: An exposition of mobile commerce in social media reviews

Peer reviewe

Real Estate Investment Trusts (REITs) Corporate Governance and Investment Decision-Making in the United Kingdom, South Africa and Nigeria

Adopting Real Estate Investment Trusts (REITs) has been relatively slow due to corporate governance issues and a limited understanding of investment decision-making processes. This study aims to enhance the performance of REITs by developing a Corporate Governance Scoring Framework and improving the investment decision-making process. A mixed-method research strategy was employed to gather data on investment decisionmaking processes and corporate governance in the UK, SA, and Nigeria from 2014-2019. Qualitative data was collected through semi-structured telephone interviews with key decision-makers in the three regimes and analysed using content and discourse analysis techniques. Quantitative data was obtained from the annual financial reports of listed REITs during the study period and analysed using OLS, fixed effects, and random effect models. The Integrated Corporate Governance Index (ICGI), a self-scoring framework, was used to measure the quality of corporate governance strength. The qualitative analysis identified four stages in the investment decision-making process: strategy, search, analysis and adjustment, and consultation or decision and review. The interviews revealed that the board, remuneration, and fee proxies were relevant factors across all three regimes, with audit and ownership also significant in the developing regimes of SA and Nigeria. The board's reputation, experience, and management role were highlighted as crucial during the decision-making process. Performance factors such as 'Operational Stability,' 'Tenant Quality,' 'Experience,' and metrics including 'Rental Income,' 'Dividend Payment,' and 'Yield' were identified. The quantitative analysis demonstrated that adherence to corporate governance codes was highest in the UK, followed by SA and Nigeria. Regression analysis results showed that a higher ICGI score improved return on assets (ROA) and return on equity (ROE) in the UK but not in SA and Nigeria. The index did not significantly impact firm value in the UK and pooled country analysis, but it led to better firm valuation in SA. In the Nigeria REIT regime, the ICGI harmed firm valuation. The study concluded that adherence to country-level corporate governance was more predictive of operational performance than firm valuation. In summary, this study contributes to the existing knowledge by providing insights into the investment decision-making processes of REITs and the importance of corporate governance in improving their performance. The developed Corporate Governance Scoring Framework offers a valuable tool for evaluating the quality of corporate governance in REITs, but further refinement is necessary to keep up with evolving policies