857 research outputs found
Mobilizing the Trump Train: Understanding Collective Action in a Political Trolling Community
Political trolls initiate online discord not only for the lulz (laughs) but
also for ideological reasons, such as promoting their desired political
candidates. Political troll groups recently gained spotlight because they were
considered central in helping Donald Trump win the 2016 US presidential
election, which involved difficult mass mobilizations. Political trolls face
unique challenges as they must build their own communities while simultaneously
disrupting others. However, little is known about how political trolls mobilize
sufficient participation to suddenly become problems for others. We performed a
quantitative longitudinal analysis of more than 16 million comments from one of
the most popular and disruptive political trolling communities, the subreddit
/r/The\_Donald (T\D). We use T_D as a lens to understand participation and
collective action within these deviant spaces. In specific, we first study the
characteristics of the most active participants to uncover what might drive
their sustained participation. Next, we investigate how these active
individuals mobilize their community to action. Through our analysis, we
uncover that the most active employed distinct discursive strategies to
mobilize participation, and deployed technical tools like bots to create a
shared identity and sustain engagement. We conclude by providing data-backed
design implications for designers of civic media
Who let the trolls out? Towards understanding state-sponsored trolls
Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused ?trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's in- formation ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence detection is not straightforward. Using Hawkes Processes, we quantify the influence these accounts have on pushing URLs on four platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab. In general, Russian trolls were more influential and efficient in pushing URLs to all the other platforms with the exception of /pol/ where Iranians were more influential. Finally, we release our source code to ensure the reproducibility of our results and to encourage other researchers to work on understanding other emerging kinds of state-sponsored troll accounts on Twitter.https://arxiv.org/pdf/1811.03130.pdfAccepted manuscrip
Like trainer, like bot? Inheritance of bias in algorithmic content moderation
The internet has become a central medium through which `networked publics'
express their opinions and engage in debate. Offensive comments and personal
attacks can inhibit participation in these spaces. Automated content moderation
aims to overcome this problem using machine learning classifiers trained on
large corpora of texts manually annotated for offence. While such systems could
help encourage more civil debate, they must navigate inherently normatively
contestable boundaries, and are subject to the idiosyncratic norms of the human
raters who provide the training data. An important objective for platforms
implementing such measures might be to ensure that they are not unduly biased
towards or against particular norms of offence. This paper provides some
exploratory methods by which the normative biases of algorithmic content
moderation systems can be measured, by way of a case study using an existing
dataset of comments labelled for offence. We train classifiers on comments
labelled by different demographic subsets (men and women) to understand how
differences in conceptions of offence between these groups might affect the
performance of the resulting models on various test sets. We conclude by
discussing some of the ethical choices facing the implementers of algorithmic
moderation systems, given various desired levels of diversity of viewpoints
amongst discussion participants.Comment: 12 pages, 3 figures, 9th International Conference on Social
Informatics (SocInfo 2017), Oxford, UK, 13--15 September 2017 (forthcoming in
Springer Lecture Notes in Computer Science
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection
Neural networks are increasingly used for graph classification in a variety
of contexts. Social media is a critical application area in this space, however
the characteristics of social media graphs differ from those seen in most
popular benchmark datasets. Social networks tend to be large and sparse, while
benchmarks are small and dense. Classically, large and sparse networks are
analyzed by studying the distribution of local properties. Inspired by this, we
introduce Graph-Hist: an end-to-end architecture that extracts a graph's latent
local features, bins nodes together along 1-D cross sections of the feature
space, and classifies the graph based on this multi-channel histogram. We show
that Graph-Hist improves state of the art performance on true social media
benchmark datasets, while still performing well on other benchmarks. Finally,
we demonstrate Graph-Hist's performance by conducting bot detection in social
media. While sophisticated bot and cyborg accounts increasingly evade
traditional detection methods, they leave artificial artifacts in their
conversational graph that are detected through graph classification. We apply
Graph-Hist to classify these conversational graphs. In the process, we confirm
that social media graphs are different than most baselines and that Graph-Hist
outperforms existing bot-detection models
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