11,095 research outputs found
Impact Of Content Features For Automatic Online Abuse Detection
Online communities have gained considerable importance in recent years due to
the increasing number of people connected to the Internet. Moderating user
content in online communities is mainly performed manually, and reducing the
workload through automatic methods is of great financial interest for community
maintainers. Often, the industry uses basic approaches such as bad words
filtering and regular expression matching to assist the moderators. In this
article, we consider the task of automatically determining if a message is
abusive. This task is complex since messages are written in a non-standardized
way, including spelling errors, abbreviations, community-specific codes...
First, we evaluate the system that we propose using standard features of online
messages. Then, we evaluate the impact of the addition of pre-processing
strategies, as well as original specific features developed for the community
of an online in-browser strategy game. We finally propose to analyze the
usefulness of this wide range of features using feature selection. This work
can lead to two possible applications: 1) automatically flag potentially
abusive messages to draw the moderator's attention on a narrow subset of
messages ; and 2) fully automate the moderation process by deciding whether a
message is abusive without any human intervention
Graph-based Features for Automatic Online Abuse Detection
While online communities have become increasingly important over the years,
the moderation of user-generated content is still performed mostly manually.
Automating this task is an important step in reducing the financial cost
associated with moderation, but the majority of automated approaches strictly
based on message content are highly vulnerable to intentional obfuscation. In
this paper, we discuss methods for extracting conversational networks based on
raw multi-participant chat logs, and we study the contribution of graph
features to a classification system that aims to determine if a given message
is abusive. The conversational graph-based system yields unexpectedly high
performance , with results comparable to those previously obtained with a
content-based approach
Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features
In recent years, online social networks have allowed worldwide users to meet
and discuss. As guarantors of these communities, the administrators of these
platforms must prevent users from adopting inappropriate behaviors. This
verification task, mainly done by humans, is more and more difficult due to the
ever growing amount of messages to check. Methods have been proposed to
automatize this moderation process, mainly by providing approaches based on the
textual content of the exchanged messages. Recent work has also shown that
characteristics derived from the structure of conversations, in the form of
conversational graphs, can help detecting these abusive messages. In this
paper, we propose to take advantage of both sources of information by proposing
fusion methods integrating content-and graph-based features. Our experiments on
raw chat logs show that the content of the messages, but also of their dynamics
within a conversation contain partially complementary information, allowing
performance improvements on an abusive message classification task with a final
F-measure of 93.26%
Hybrid moderation in the newsroom: Recommending featured posts to content moderators
Online news outlets are grappling with the moderation of user-generated
content within their comment section. We present a recommender system based on
ranking class probabilities to support and empower the moderator in choosing
featured posts, a time-consuming task. By combining user and textual content
features we obtain an optimal classification F1-score of 0.44 on the test set.
Furthermore, we observe an optimum mean NDCG@5 of 0.87 on a large set of
validation articles. As an expert evaluation, content moderators assessed the
output of a random selection of articles by choosing comments to feature based
on the recommendations, which resulted in a NDCG score of 0.83. We conclude
that first, adding text features yields the best score and second, while
choosing featured content remains somewhat subjective, content moderators found
suitable comments in all but one evaluated recommendations. We end the paper by
analyzing our best-performing model, a step towards transparency and
explainability in hybrid content moderation
Presenting a Labelled Dataset for Real-Time Detection of Abusive User Posts
Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, we present a dataset of user comments, using crowdsourcing for labelling. Since abusive content can be ambiguous and subjective to the individual reader, we propose an aggregated mechanism for assessing different opinions from different labellers. In addition, instead of the typical binary categories of abusive or not, we introduce a third class of âundecidedâ to capture the real life scenario of instances that are neither blatantly abusive nor clearly harmless. We have performed preliminary experiments on this dataset using best practice techniques in text classification. Finally, we have evaluated the detection performance of various feature groups, namely syntactic, semantic and context-based features. Results show these features can increase our classifier performance by 18% in detection of abusive content
BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology
This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software
Social Bots: Human-Like by Means of Human Control?
Social bots are currently regarded an influential but also somewhat
mysterious factor in public discourse and opinion making. They are considered
to be capable of massively distributing propaganda in social and online media
and their application is even suspected to be partly responsible for recent
election results. Astonishingly, the term `Social Bot' is not well defined and
different scientific disciplines use divergent definitions. This work starts
with a balanced definition attempt, before providing an overview of how social
bots actually work (taking the example of Twitter) and what their current
technical limitations are. Despite recent research progress in Deep Learning
and Big Data, there are many activities bots cannot handle well. We then
discuss how bot capabilities can be extended and controlled by integrating
humans into the process and reason that this is currently the most promising
way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure
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