12,663 research outputs found
Crowdsourced Rumour Identification During Emergencies
When a significant event occurs, many social media users leverage platforms such as Twitter to track that event. Moreover, emergency response agencies are increasingly looking to social media as a source of real-time information about such events. However, false information and rumours are often spread during such events, which can influence public opinion and limit the usefulness of social media for emergency management. In this paper, we present an initial study into rumour identification during emergencies using crowdsourcing. In particular, through an analysis of three tweet datasets relating to emergency events from 2014, we propose a taxonomy of tweets relating to rumours. We then perform a crowdsourced labeling experiment to determine whether crowd assessors can identify rumour-related tweets and where such labeling can fail. Our results show that overall, agreement over the tweet labels produced were high (0.7634 Fleiss Kappa), indicating that crowd-based rumour labeling is possible. However, not all tweets are of equal difficulty to assess. Indeed, we show that tweets containing disputed/controversial information tend to be some of the most difficult to identify
Storia: Summarizing Social Media Content based on Narrative Theory using Crowdsourcing
People from all over the world use social media to share thoughts and
opinions about events, and understanding what people say through these channels
has been of increasing interest to researchers, journalists, and marketers
alike. However, while automatically generated summaries enable people to
consume large amounts of data efficiently, they do not provide the context
needed for a viewer to fully understand an event. Narrative structure can
provide templates for the order and manner in which this data is presented to
create stories that are oriented around narrative elements rather than
summaries made up of facts. In this paper, we use narrative theory as a
framework for identifying the links between social media content. To do this,
we designed crowdsourcing tasks to generate summaries of events based on
commonly used narrative templates. In a controlled study, for certain types of
events, people were more emotionally engaged with stories created with
narrative structure and were also more likely to recommend them to others
compared to summaries created without narrative structure
Neural Based Statement Classification for Biased Language
Biased language commonly occurs around topics which are of controversial
nature, thus, stirring disagreement between the different involved parties of a
discussion. This is due to the fact that for language and its use,
specifically, the understanding and use of phrases, the stances are cohesive
within the particular groups. However, such cohesiveness does not hold across
groups.
In collaborative environments or environments where impartial language is
desired (e.g. Wikipedia, news media), statements and the language therein
should represent equally the involved parties and be neutrally phrased. Biased
language is introduced through the presence of inflammatory words or phrases,
or statements that may be incorrect or one-sided, thus violating such
consensus.
In this work, we focus on the specific case of phrasing bias, which may be
introduced through specific inflammatory words or phrases in a statement. For
this purpose, we propose an approach that relies on a recurrent neural networks
in order to capture the inter-dependencies between words in a phrase that
introduced bias.
We perform a thorough experimental evaluation, where we show the advantages
of a neural based approach over competitors that rely on word lexicons and
other hand-crafted features in detecting biased language. We are able to
distinguish biased statements with a precision of P=0.92, thus significantly
outperforming baseline models with an improvement of over 30%. Finally, we
release the largest corpus of statements annotated for biased language.Comment: The Twelfth ACM International Conference on Web Search and Data
Mining, February 11--15, 2019, Melbourne, VIC, Australi
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDOS) attacks, data breaches, and account hijacking) in an
unsupervised manner using just a limited fixed set of seed event triggers. A
new query expansion strategy based on convolutional kernels and dependency
parses helps model reporting structure and aids in identifying key event
characteristics. Through a large-scale analysis over Twitter, we demonstrate
that our approach consistently identifies and encodes events, outperforming
existing methods.Comment: 13 single column pages, 5 figures, submitted to KDD 201
Target Apps Selection: Towards a Unified Search Framework for Mobile Devices
With the recent growth of conversational systems and intelligent assistants
such as Apple Siri and Google Assistant, mobile devices are becoming even more
pervasive in our lives. As a consequence, users are getting engaged with the
mobile apps and frequently search for an information need in their apps.
However, users cannot search within their apps through their intelligent
assistants. This requires a unified mobile search framework that identifies the
target app(s) for the user's query, submits the query to the app(s), and
presents the results to the user. In this paper, we take the first step forward
towards developing unified mobile search. In more detail, we introduce and
study the task of target apps selection, which has various potential real-world
applications. To this aim, we analyze attributes of search queries as well as
user behaviors, while searching with different mobile apps. The analyses are
done based on thousands of queries that we collected through crowdsourcing. We
finally study the performance of state-of-the-art retrieval models for this
task and propose two simple yet effective neural models that significantly
outperform the baselines. Our neural approaches are based on learning
high-dimensional representations for mobile apps. Our analyses and experiments
suggest specific future directions in this research area.Comment: To appear at SIGIR 201
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
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