1,086 research outputs found
On the influence of social bots in online protests. Preliminary findings of a Mexican case study
Social bots can affect online communication among humans. We study this
phenomenon by focusing on #YaMeCanse, the most active protest hashtag in the
history of Twitter in Mexico. Accounts using the hashtag are classified using
the BotOrNot bot detection tool. Our preliminary analysis suggests that bots
played a critical role in disrupting online communication about the protest
movement.Comment: 10 page
Can electoral popularity be predicted using socially generated big data?
Today, our more-than-ever digital lives leave significant footprints in
cyberspace. Large scale collections of these socially generated footprints,
often known as big data, could help us to re-investigate different aspects of
our social collective behaviour in a quantitative framework. In this
contribution we discuss one such possibility: the monitoring and predicting of
popularity dynamics of candidates and parties through the analysis of socially
generated data on the web during electoral campaigns. Such data offer
considerable possibility for improving our awareness of popularity dynamics.
However they also suffer from significant drawbacks in terms of
representativeness and generalisability. In this paper we discuss potential
ways around such problems, suggesting the nature of different political systems
and contexts might lend differing levels of predictive power to certain types
of data source. We offer an initial exploratory test of these ideas, focussing
on two data streams, Wikipedia page views and Google search queries. On the
basis of this data, we present popularity dynamics from real case examples of
recent elections in three different countries.Comment: To appear in Information Technolog
Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data
Use of socially generated "big data" to access information about collective
states of the minds in human societies has become a new paradigm in the
emerging field of computational social science. A natural application of this
would be the prediction of the society's reaction to a new product in the sense
of popularity and adoption rate. However, bridging the gap between "real time
monitoring" and "early predicting" remains a big challenge. Here we report on
an endeavor to build a minimalistic predictive model for the financial success
of movies based on collective activity data of online users. We show that the
popularity of a movie can be predicted much before its release by measuring and
analyzing the activity level of editors and viewers of the corresponding entry
to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the
dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi
Does Campaigning on Social Media Make a Difference? Evidence from candidate use of Twitter during the 2015 and 2017 UK Elections
Social media are now a routine part of political campaigns all over the
world. However, studies of the impact of campaigning on social platform have
thus far been limited to cross-sectional datasets from one election period
which are vulnerable to unobserved variable bias. Hence empirical evidence on
the effectiveness of political social media activity is thin. We address this
deficit by analysing a novel panel dataset of political Twitter activity in the
2015 and 2017 elections in the United Kingdom. We find that Twitter based
campaigning does seem to help win votes, a finding which is consistent across a
variety of different model specifications including a first difference
regression. The impact of Twitter use is small in absolute terms, though
comparable with that of campaign spending. Our data also support the idea that
effects are mediated through other communication channels, hence challenging
the relevance of engaging in an interactive fashion
Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump
Measuring and forecasting opinion trends from real-time social media is a
long-standing goal of big-data analytics. Despite its importance, there has
been no conclusive scientific evidence so far that social media activity can
capture the opinion of the general population. Here we develop a method to
infer the opinion of Twitter users regarding the candidates of the 2016 US
Presidential Election by using a combination of statistical physics of complex
networks and machine learning based on hashtags co-occurrence to develop an
in-domain training set approaching 1 million tweets. We investigate the social
networks formed by the interactions among millions of Twitter users and infer
the support of each user to the presidential candidates. The resulting Twitter
trends follow the New York Times National Polling Average, which represents an
aggregate of hundreds of independent traditional polls, with remarkable
accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls
by 10 days, showing that Twitter can be an early signal of global opinion
trends. Our analytics unleash the power of Twitter to uncover social trends
from elections, brands to political movements, and at a fraction of the cost of
national polls
2020 General Presidential Debates: The Coronavirus Clash
In the run up to the 2020 election on November 3, 2020, two presidential and one vice presidential debate were held (another planned presidential debate was cancelled because of coronavirus). The presidential debates used attacks more than acclaims – and more than previous debates (the vice presidential debate was fairly similar to previous VP debates). Biden and Trump discussed policy more than character (as did the VP debate and previous presidential and vice presidential debates). Unlike most previous encounters, conflicting with the theoretical prediction and in contrast to the vice presidential debate, the two Biden Trump debates in 2020 attacked more than they acclaimed. All three debates emphasized policy more than character, in line with theory and past research
Three essays on political economy
This thesis comprises three papers on political economy. We study how politicians are selected during elections in the first two papers. In the first paper, we study the individual characteristics (such as education, job, and experience) that render some candidates more successful than others. In the second paper, we study how information about a candidate’s characteristics affects voter behavior through a field/online experiment. While in the third paper, we introduce a new dataset and a methodological approach to retrieve granular precinct-level electoral results
sentiment analysis in tweets during an electoral period
Rita, P., António, N., & Afonso, A. P. (2023). Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period. Social Network Analysis and Mining, 13(1), 1-16. [46]. https://doi.org/10.1007/s13278-023-01048-1 --- Funding: Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.In a time where social media is fundamental for any political campaign and to share a message with an electoral audience, this study searches for a conclusion of the actual persuasion capacity of social media in the electors when they need to decide whom to vote for as their next government. For this, it compares the sentiment that Social Media users demonstrated during an electoral period with the actual results of those elections. For this analysis, it was used, as a case study, tweets mentioning the two major English parties, Conservative and Labor, their respective candidates for the position of prime minister, and terms that identified their political campaign during the electoral period of the General Elections of the United Kingdom that occurred on December 12, 2019. Data were collected using R. The treatment and analysis were done with R and RapidMiner. Results show that tweets’ sentiment is not a reliable election results predictor. Additionally, results also show that it is impossible to state that social media impacts voting decisions. At least not from the polarity of the sentiment of opinions on social media.publishersversionepub_ahead_of_prin
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