33,223 research outputs found
Classifying Twitter Favorites: Like, Bookmark, or Thanks?
Since its foundation in 2006, Twitter has enjoyed a meteoric
rise in popularity, currently boasting over 500
million users. Its short text nature means that the service
is open to a variety of different usage patterns, which
have evolved rapidly in terms of user base and utilization.
Prior work has categorized Twitter users, as well as
studied the use of lists and re-tweets and how these can
be used to infer user profiles and interests. The focus of
this article is on studying why and how Twitter users
mark tweets as “favorites”—a functionality with currently
poorly understood usage, but strong relevance for
personalization and information access applications.
Firstly, manual analysis and classification are carried
out on a randomly chosen set of favorited tweets, which
reveal different approaches to using this functionality
(i.e., bookmarks, thanks, like, conversational, and selfpromotion).
Secondly, an automatic favorites classification
approach is proposed, based on the categories
established in the previous step. Our machine learning
experiments demonstrate a high degree of success in
matching human judgments in classifying favorites
according to usage type. In conclusion, we discuss the
purposes to which these data could be put, in the
context of identifying users’ patterns of interests
A Socio-Informatic Approach to Automated Account Classification on Social Media
Automated accounts on social media have become increasingly problematic. We
propose a key feature in combination with existing methods to improve machine
learning algorithms for bot detection. We successfully improve classification
performance through including the proposed feature.Comment: International Conference on Social Media and Societ
Organized Behavior Classification of Tweet Sets using Supervised Learning Methods
During the 2016 US elections Twitter experienced unprecedented levels of
propaganda and fake news through the collaboration of bots and hired persons,
the ramifications of which are still being debated. This work proposes an
approach to identify the presence of organized behavior in tweets. The Random
Forest, Support Vector Machine, and Logistic Regression algorithms are each
used to train a model with a data set of 850 records consisting of 299 features
extracted from tweets gathered during the 2016 US presidential election. The
features represent user and temporal synchronization characteristics to capture
coordinated behavior. These models are trained to classify tweet sets among the
categories: organic vs organized, political vs non-political, and pro-Trump vs
pro-Hillary vs neither. The random forest algorithm performs better with
greater than 95% average accuracy and f-measure scores for each category. The
most valuable features for classification are identified as user based
features, with media use and marking tweets as favorite to be the most
dominant.Comment: 51 pages, 5 figure
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
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