1,553 research outputs found
Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter
Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world. The vast amount of readily-available data on such platforms, coupled with the emerging power of natural language processing algorithms and tools, have made it possible to mine and generate foresight into the possible directions of elections' outcome. In this paper, lexicon-based public emotion mining and sentiment analysis were conducted to predict win in the 2019 presidential election in Nigeria. 224,500 tweets, associated with the two most prominent political parties in Nigeria, People's Democratic Party (PDP) and All Progressive Congress (APC), and the two most prominent presidential candidates that represented these parties in the 2019 elections, Atiku Abubakar and Muhammadu Buhari, were collected between 9th October 2018 and 17th December 2018 via the Twitter's streaming API. tm and NRC libraries, defined in the 'R' integrated development environment, were used for data cleaning and preprocessing purposes. Botometer was introduced to detect the presence of automated bots in the preprocessed data while NRC Word Emotion Association Lexicon (EmoLex) was used to generate distributions of subjective public sentiments and emotions that surround the Nigerian 2019 presidential election. Emotions were grouped into eight categories (sadness, trust, anger, fear, joy, anticipation, disgust, surprise) while sentiments were grouped into two (negative and positive) based on Plutchik's emotion wheel. Results obtained indicate a higher positive and a lower negative sentiment for APC than was observed with PDP. Similarly, for the presidential aspirants, Atiku has a slightly higher positive and a slightly lower negative sentiment than was observed with Buhari. These results show that APC is the predicted winning party and Atiku as the most preferred winner of the 2019 presidential election. These predictions were corroborated by the actual election results as APC emerged as the winning party while Buhari and Atiku shared very close vote margin in the election. Hence, this research is an indication that twitter data can be appropriately used to predict election outcomes and other offline future events. Future research could investigate spatiotemporal dimensions of the prediction
On predictability of rare events leveraging social media: a machine learning perspective
Information extracted from social media streams has been leveraged to
forecast the outcome of a large number of real-world events, from political
elections to stock market fluctuations. An increasing amount of studies
demonstrates how the analysis of social media conversations provides cheap
access to the wisdom of the crowd. However, extents and contexts in which such
forecasting power can be effectively leveraged are still unverified at least in
a systematic way. It is also unclear how social-media-based predictions compare
to those based on alternative information sources. To address these issues,
here we develop a machine learning framework that leverages social media
streams to automatically identify and predict the outcomes of soccer matches.
We focus in particular on matches in which at least one of the possible
outcomes is deemed as highly unlikely by professional bookmakers. We argue that
sport events offer a systematic approach for testing the predictive power of
social media, and allow to compare such power against the rigorous baselines
set by external sources. Despite such strict baselines, our framework yields
above 8% marginal profit when used to inform simple betting strategies. The
system is based on real-time sentiment analysis and exploits data collected
immediately before the games, allowing for informed bets. We discuss the
rationale behind our approach, describe the learning framework, its prediction
performance and the return it provides as compared to a set of betting
strategies. To test our framework we use both historical Twitter data from the
2014 FIFA World Cup games, and real-time Twitter data collected by monitoring
the conversations about all soccer matches of four major European tournaments
(FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA
Champions League, during the period between Oct. 25th 2014 and Nov. 26th 2014.Comment: 10 pages, 10 tables, 8 figure
Natural Language Processing for Prediction of Election Results on Twitter Engagement and Polls
With the ability to predict political outcomes and provide insights into public opinion, using Twitter data to predict election results has gained popularity. Twitter offers a massive supply of data for analysis due to its enormous user base and real-time nature. Researchers use sentiment analysis tools to categorize tweets as good, harmful, or neutral and follow sentiment patterns over time. Network analysis finds influential users and digs deeper into the dynamics of political discourse. The accuracy of predictions is improved by combining traditional polling data with machine learning methods. Twitter data analysis has the potential to offer insightful information for election campaigns and improve political strategies despite issues like representativeness and identifying genuine sentiment. Ongoing research focuses on refining methodologies and addressing limitations, advancing the reliability of election prediction using Twitter data. The paper shows the results of election prediction for Indian political parties based on Twitter dat
Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election
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 paper 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
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Why polls fail to predict elections
In the past decade we have witnessed the failure of traditional polls in
predicting presidential election outcomes across the world. To understand the
reasons behind these failures we analyze the raw data of a trusted pollster
which failed to predict, along with the rest of the pollsters, the surprising
2019 presidential election in Argentina which has led to a major market
collapse in that country. Analysis of the raw and re-weighted data from
longitudinal surveys performed before and after the elections reveals clear
biases (beyond well-known low-response rates) related to mis-representation of
the population and, most importantly, to social-desirability biases, i.e., the
tendency of respondents to hide their intention to vote for controversial
candidates. We then propose a longitudinal opinion tracking method based on
big-data analytics from social media, machine learning, and network theory that
overcomes the limits of traditional polls. The model achieves accurate results
in the 2019 Argentina elections predicting the overwhelming victory of the
candidate Alberto Fern\'andez over the president Mauricio Macri; a result that
none of the traditional pollsters in the country was able to predict. Beyond
predicting political elections, the framework we propose is more general and
can be used to discover trends in society; for instance, what people think
about economics, education or climate change.Comment: 47 pages, 10 tables, 15 figure
Predictive Analysis on Twitter: Techniques and Applications
Predictive analysis of social media data has attracted considerable attention
from the research community as well as the business world because of the
essential and actionable information it can provide. Over the years, extensive
experimentation and analysis for insights have been carried out using Twitter
data in various domains such as healthcare, public health, politics, social
sciences, and demographics. In this chapter, we discuss techniques, approaches
and state-of-the-art applications of predictive analysis of Twitter data.
Specifically, we present fine-grained analysis involving aspects such as
sentiment, emotion, and the use of domain knowledge in the coarse-grained
analysis of Twitter data for making decisions and taking actions, and relate a
few success stories
Una revisión del análisis político mediante la web social
En los países democráticos, conocer la intención de voto de los ciudadanos y las valoraciones de los principales partidos y líderes políticos es de gran interés tanto para los propios partidos como para los medios de comunicación y el público en general. Para ello se han utilizado tradicionalmente costosas encuestas personales. El auge de las redes sociales, principalmente Twitter, permite pensar en ellas como una alternativa barata a las encuestas. En este trabajo, revisamos la bibliografía científica más relevante en este ámbito, poniendo especial énfasis en el caso español.In democratic countries, forecasting the voting intentions of citizens and knowing their opinions on major political parties and leaders is of great interest to the parties themselves, to the media, and to the general public. Traditionally, expensive polls based on personal interviews have been used for this purpose. The rise of social networks, particularly Twitter, allows us to consider them as a cheap alternative. In this paper, we review the relevant scientific bibliographic references in this area, with special emphasis on the Spanish case.This research is partially supported by Ministerio de Economía y Competitividad (FFI2014-51978-C2). David Vilares is partially funded by the Ministerio de Educación, Cultura y Deporte (FPU13/01180)
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