9 research outputs found
The Effect of Collective Attention on Controversial Debates on Social Media
We study the evolution of long-lived controversial debates as manifested on
Twitter from 2011 to 2016. Specifically, we explore how the structure of
interactions and content of discussion varies with the level of collective
attention, as evidenced by the number of users discussing a topic. Spikes in
the volume of users typically correspond to external events that increase the
public attention on the topic -- as, for instance, discussions about `gun
control' often erupt after a mass shooting.
This work is the first to study the dynamic evolution of polarized online
debates at such scale. By employing a wide array of network and content
analysis measures, we find consistent evidence that increased collective
attention is associated with increased network polarization and network
concentration within each side of the debate; and overall more uniform lexicon
usage across all users.Comment: accepted at ACM WebScience 201
Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events
Social media datasets make it possible to rapidly quantify collective
attention to emerging topics and breaking news, such as crisis events.
Collective attention is typically measured by aggregate counts, such as the
number of posts that mention a name or hashtag. But according to rationalist
models of natural language communication, the collective salience of each
entity will be expressed not only in how often it is mentioned, but in the form
that those mentions take. This is because natural language communication is
premised on (and customized to) the expectations that speakers and writers have
about how their messages will be interpreted by the intended audience. We test
this idea by conducting a large-scale analysis of public online discussions of
breaking news events on Facebook and Twitter, focusing on five recent crisis
events. We examine how people refer to locations, focusing specifically on
contextual descriptors, such as "San Juan" versus "San Juan, Puerto Rico."
Rationalist accounts of natural language communication predict that such
descriptors will be unnecessary (and therefore omitted) when the named entity
is expected to have high prior salience to the reader. We find that the use of
contextual descriptors is indeed associated with proxies for social and
informational expectations, including macro-level factors like the location's
global salience and micro-level factors like audience engagement. We also find
a consistent decrease in descriptor context use over the lifespan of each
crisis event. These findings provide evidence about how social media users
communicate with their audiences, and point towards more fine-grained models of
collective attention that may help researchers and crisis response
organizations to better understand public perception of unfolding crisis
events.Comment: ICWSM 202
Unravelling the truth: Examining the evidence for health-related claims made by naturopathic influencers on social media – a retrospective analysis
Background: Social media platforms are frequently used by the general public to access health information, including information relating to complementary and alternative medicine (CAM). The aim of this study was to measure how often naturopathic influencers make evidence-informed recommendations on Instagram, and to examine associations between the level of evidence available or presented, and user engagement. Methods: A retrospective observational study using quantitative content analysis on health-related claims made by naturopathic influencers with 30000 or more followers on Instagram was conducted. Linear regression was used to measure the association between health-related posts and the number of Likes, and Comments. Results: A total of 494 health claims were extracted from eight Instagram accounts, of which 242 (49.0%) were supported by evidence and 34 (6.9%) included a link to evidence supporting the claim. Three naturopathic influencers did not provide any evidence to support the health claims they made on Instagram. Posts with links to evidence had fewer Likes (B=-1343.9, 95% CI=-2424.4 to -263.4, X=-0.1, P=0.02) and fewer Comments (B=-82.0, 95% CI=-145.9 to -18.2, X=-0.2, P=0.01), compared to posts without links to evidence. The most common areas of health were claims relating to ‘women’s health’ (n=94; 19.0%), and ‘hair, nail and skin’ (n=74; 15.0%). Conclusion: This study is one of the first to look at the evidence available to support health-related claims by naturopathic influencers on Instagram. Our findings indicate that around half of Instagram posts from popular naturopathic influencers with health claims are supported by high-quality evidence
Bounded Confidence: How AI Could Exacerbate Social Media’s Homophily Problem
The advent of the Internet was heralded as a revolutionary development in the democratization of information. It has emerged, however, that online discourse on social media tends to narrow the information landscape of its users. This dynamic is driven by the propensity of the network structure of social media to tend toward homophily; users strongly prefer to interact with content and other users that are similar to them. We review the considerable evidence for the ubiquity of homophily in social media, discuss some possible mechanisms for this phenomenon, and present some observed and hypothesized effects. We also discuss how the homophilic structure of social media makes it uniquely vulnerable to artificial-intelligence-driven, automated influence campaigns
A Content Analysis: Examining Facebook Comments on News Media Posts For Echo Chambers
Social media serves as a way for people to connect. People can choose who they connect with, this can cause echo chambers to appear online and can also cause in/out groups to become present. This thesis will examine echo chambers and in/out groups using a content analysis of comments on news media posts on Facebook. The two issues being examined are abortion and immigration. We will be looking at three news media pages: Fox News, MSNBC, and ABC News. What the content analysis will seek out to find is that the partisan news sources Fox News and MSNBC will have echo chambers of information and will attack a person of the out-group if they comment. These echo chambers and in/out-group behaviors could lead to other problems such as polarization
Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter
The reappearance of measles in the US and Europe, a disease considered
eliminated in early 2000s, has been accompanied by a growing debate on the
merits of vaccination on social media. In this study we examine the extent to
which the vaccination debate on Twitter is conductive to potential outreach to
the vaccination hesitant. We focus on Italy, one of the countries most affected
by the latest measles outbreaks. We discover that the vaccination skeptics, as
well as the advocates, reside in their own distinct "echo chambers". The
structure of these communities differs as well, with skeptics arranged in a
tightly connected cluster, and advocates organizing themselves around few
authoritative hubs. At the center of these echo chambers we find the ardent
supporters, for which we build highly accurate network- and content-based
classifiers (attaining 95% cross-validated accuracy). Insights of this study
provide several avenues for potential future interventions, including
network-guided targeting, accounting for the political context, and monitoring
of alternative sources of information
The laws of "LOL": Computational approaches to sociolinguistic variation in online discussions
When speaking or writing, a person often chooses one form of language over another based on social constraints, including expectations in a conversation, participation in a global change, or expression of underlying attitudes. Sociolinguistic variation (e.g. choosing "going" versus "goin'") can reveal consistent social differences such as dialects and consistent social motivations such as audience design. While traditional sociolinguistics studies variation in spoken communication, computational sociolinguistics investigates written communication on social media. The structured nature of online discussions and the diversity of language patterns allow computational sociolinguists to test highly specific hypotheses about communication, such different configurations of listener "audience." Studying communication choices in online discussions sheds light on long-standing sociolinguistic questions that are hard to tackle, and helps social media platforms anticipate their members' complicated patterns of participation in conversations.
To that end, this thesis explores open questions in sociolinguistic research by quantifying language variation patterns in online discussions. I leverage the "birds-eye" view of social media to focus on three major questions in sociolinguistics research relating to authors' participation in online discussions. First, I test the role of conversation expectations in the context of content bans and crisis events, and I show that authors vary their language to adjust to audience expectations in line with community standards and shared knowledge. Next, I investigate language change in online discussions and show that language structure, more than social context, explains word adoption. Lastly, I investigate the expression of social attitudes among multilingual speakers, and I find that such attitudes can explain language choice when the attitudes have a clear social meaning based on the discussion context. This thesis demonstrates the rich opportunities that social media provides for addressing sociolinguistic questions and provides insight into how people adapt to the communication affordances in online platforms.Ph.D