Information Retrieval and Machine Learning Methods for Academic Expert Finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.Spanish “Agencia Estatal de Investigación” under grants PID2019-106758GB-C31 and PID2020-113230RB-C22Spanish “FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades” under grant A-TIC-146-UGR20European Regional Development Fund (ERDF-FEDER

Information retrieval and machine learning methods for academic expert finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.Agencia Estatal de Investigación | Ref. PID2019-106758GB-C31Agencia Estatal de Investigación | Ref. PID2020-113230RB-C22FEDER/Junta de Andalucía | Ref. A-TIC-146-UGR2

Identifying Expert Investors on Financial Microblog via Artificial Neural Networks

In the recent years, thanks to social media platform, a plethora of information has been available to financial investors, that were traditionally dependent from financial institutions advisors. Strategies are now shared among web users, performances of stocks are commented in web communities and hints and suggestions are travelling on the internet with a fast pace, in a way that was unthinkable few years before. Several attempts have been made in the recent past, to predict Market movements and trends from activity of Financial Social Networks participants, and to evaluate if contributions from individuals with high level of expertise distinguish themselves from the rest of crowd. The Present Work is leveraging 6 years of tweets extracted from the financial platform StockTwits.com, deep diving in its content, and proposing a predictive Neural Network algorithm of Multi-Layer Perceptron type, based on features derived from text, social network and sentiment analysis. Users have been classified based on the performance achieved during the training, consistence of their prediction has been verified throughout the time and, finally, a trading strategy has been proposed based on following the top actors. The outcomes highlighted that expert investors are outperforming the wisdom of the crowd, and the trading schema put together generated a return of 38.6%, in 2015, when S&P500 had a slightly negative balance

Análisis de influenciadores en Twitter : Una exploración en el ámbito del mercado NASDAQ

El propósito de este trabajo es realizar una exploración de los principales influenciadores en la red social Twitter; con relación a las comunidades de discusión enfocadas en acciones negociadas en el mercado NASDAQ. De esta manera, se espera entender cómo se comportan las redes de influencia en grupos interesados en temas bursátiles, las cuales pueden conducir a que se produzca un comportamiento de pastoreo, haciendo cuestionable la hipótesis de eficiencia de los mercados. Se parte de una revisión de literatura, a partir de lo cual se conforma un marco de referencia para comprender el sentido del análisis de redes, la importancia de influenciadores y la forma en que estos afectan el comportamiento de los inversionistas. Luego, se ejecuta el análisis de redes sociales aplicando la herramienta NodeXL, con el fin de identificar los principales usuarios y las redes de influencia que se producen en la red social Twitter. Al final, los resultados muestran que, en el contexto de los mercados de valores, los influenciadores no son sólo individuos participantes en la industria financiera con conocimientos profundos en análisis bursátil; sino que líderes de opinión como políticos o empresarios pueden llegar a tener un papel central en las comunidades de inversiónThe purpose of this work is to explore the main influencers in the social network Twitter; in relation to the discussion communities focused on shares traded on the NASDAQ market. In this way, it is expected to understand how influence networks behave in groups interested in stock market issues, which can lead to grazing behavior, making the hypothesis of market efficiency questionable. It starts from a literature review, from which a frame of reference is formed to understand the meaning of network analysis, the importance of influencers and the way in which they affect the behavior of investors. Then, the analysis of social networks is executed by applying the NodeXL tool, in order to identify the main users and the networks of influence that occur in the social network Twitter. Ultimately, the results show that, in the context of equity markets, influencers are not just individuals participating in the financial industry with deep knowledge of stock analysis; rather, opinion leaders such as politicians or businessmen can play a central role in investment communities.O objetivo deste trabalho é realizar uma exploração dos principais influenciadores da rede social Twitter; em relação às comunidades de discussão voltadas para ações negociadas no mercado NASDAQ. Desta forma, espera-se compreender como se comportam as redes de influência em grupos interessados ​​em emissões de ações, o que pode levar a um comportamento de pastejo, tornando questionável a hipótese de eficiência de mercado. Parte-se de uma revisão da literatura, a partir da qual se forma um quadro de referência para compreender o significado da análise de redes, a importância dos influenciadores e a forma como eles afetam o comportamento dos investidores. Em seguida, a análise das redes sociais é executada através da aplicação da ferramenta NodeXL, a fim de identificar os principais usuários e as redes de influência que ocorrem na rede social Twitter. Em última análise, os resultados mostram que, no contexto dos mercados de ações, os influenciadores não são apenas indivíduos que participam do setor financeiro com conhecimento profundo de análise de ações; Em vez disso, líderes de opinião, como políticos ou empresários, podem desempenhar um papel central nas comunidades de investimento