190 research outputs found
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
Person-to-person evaluations are prevalent in all kinds of discourse and
important for establishing reputations, building social bonds, and shaping
public opinion. Such evaluations can be analyzed separately using signed social
networks and textual sentiment analysis, but this misses the rich interactions
between language and social context. To capture such interactions, we develop a
model that predicts individual A's opinion of individual B by synthesizing
information from the signed social network in which A and B are embedded with
sentiment analysis of the evaluative texts relating A to B. We prove that this
problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss
Markov random field, and we show that this implementation outperforms text-only
and network-only versions in two very different datasets involving
community-level decision-making: the Wikipedia Requests for Adminship corpus
and the Convote U.S. Congressional speech corpus
Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision
Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals' stances. We analyze users' attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines
Does Relational Polarization Entail Ideological Polarization? The Case of the 2017 Norwegian Election Campaign on Twitter
publishedVersio
Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods
Polarization arises when the underlying network connecting the members of a
community or society becomes characterized by highly connected groups with weak
inter-group connectivity. The increasing polarization, the strengthening of
echo chambers, and the isolation caused by information filters in social
networks are increasingly attracting the attention of researchers from
different areas of knowledge such as computer science, economics, social and
political sciences. This work presents an annotated review of network
polarization measures and models used to handle the polarization. Several
approaches for measuring polarization in graphs and networks were identified,
including those based on homophily, modularity, random walks, and balance
theory. The strategies used for reducing polarization include methods that
propose edge or node editions (including insertions or deletions, as well as
edge weight modifications), changes in social network design, or changes in the
recommendation systems embedded in these networks.Comment: Corrected a typo in Section 3.2; the rest remains unchange
Predicting Online Islamophobic Behavior after #ParisAttack
The tragic Paris terrorist attacks of November 13, 2015 sparked a massive global discussion on Twitter and other social media, with millions of tweets in the first few hours after the attacks. Most of these tweets were condemning the attacks and showing support for Parisians. One of the trending debates related to the attacks concerned possible association between Muslims and terrorism, which resulted in a world-wide debate between those attacking and those defending Islam. In this paper, we use this incident as a case study to examine using online social network interactions prior to an event to predict what attitudes will be expressed in response to the event. Specifically, we focus on how a personâs online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event. In our study, we collected a set of 8.36 million tweets related to the Paris attacks within the 50 hours following the event, of which we identified over 900k tweets mentioning Islam and Muslims. We then quantitatively analyzed usersâ network interactions and historical tweets to predict their attitudes towards Islam and Muslims. We provide a description of the quantitative results based on the tweet content (hashtags) and network interactions (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn usersâ stated stance towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stance. We found that pre-event network interactions can predict attitudes towards Muslims with 82% macro F-measure, even in the absence of prior mentions of Islam, Muslims, or related terms
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âSoftware agents and haunted media : the twitter bot as political actor"
This report examines the rhetorical construction of Twitter bots as nonhuman political agents in press coverage of the 2016 U.S. election. It takes the rhetorical framing of âthe Twitter botâ as a case study to argue that Twitter bots are a contemporary example of what media historian Jeffrey Sconce calls âhaunted mediaâ -- a communication technology that has been culturally ascribed an âuncannyâ âagency.â First, this report provides a comparative close reading of two pieces from The Atlantic and The New York Times as examples of mainstream press coverage of bots shortly before and after the 2016 U.S. presidential election. Second, drawing on Sconceâs analysis of nineteenth and twentieth century media ecologies, it argues that âthe Twitter botâ has been rhetorically constructed as haunted media through discourses that are inseparable from larger political narratives. The third and final section speculates on possible theoretical frameworks to expand this project in further inquiries. This report aims to demonstrate that haunted media narratives predate and persist beyond a specific election cycle or medium, and to argue that the construction of âhaunted mediaâ occurs alongside constructed concepts of democracy in our technologically mediated society. In doing so, this report contributes to the field of rhetoric of digital technology by bringing it further into conversation with political rhetoric.Englis
Your most telling friends: Propagating latent ideological features on Twitter using neighborhood coherence
Multidimensional scaling in networks allows for the discovery of latent
information about their structure by embedding nodes in some feature space.
Ideological scaling for users in social networks such as Twitter is an example,
but similar settings can include diverse applications in other networks and
even media platforms or e-commerce. A growing literature of ideology scaling
methods in social networks restricts the scaling procedure to nodes that
provide interpretability of the feature space: on Twitter, it is common to
consider the sub-network of parliamentarians and their followers. This allows
to interpret inferred latent features as indices for ideology-related concepts
inspecting the position of members of parliament. While effective in inferring
meaningful features, this is generally restrained to these sub-networks,
limiting interesting applications such as country-wide measurement of
polarization and its evolution. We propose two methods to propagate ideological
features beyond these sub-networks: one based on homophily (linked users have
similar ideology), and the other on structural similarity (nodes with similar
neighborhoods have similar ideologies). In our methods, we leverage the concept
of neighborhood ideological coherence as a parameter for propagation. Using
Twitter data, we produce an ideological scaling for 370K users, and analyze the
two families of propagation methods on a population of 6.5M users. We find
that, when coherence is considered, the ideology of a user is better estimated
from those with similar neighborhoods, than from their immediate neighbors.Comment: 8 pages, 2020 ASONAM Conferenc
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