4,199 research outputs found
Crowdsourcing a Text Corpus is not a Game
Building language corpora for low resource languages such as South Africa’s isiXhosa is challenging because of limited digitized texts. Language corpora are needed for building information retrieval services such as search and translation and to support further online content creation. A novel solution was proposed to source original and relevant multilingual content by crowdsourcing translations via an online competitive game where participants would be paid for their contributions. Four experiments were conducted and the results support the idea that gamification by itself does not yield the widely expected benefits of increased motivation and engagement. We found that people do not volunteer without financial incentives, the form of payment does not matter, they would not continue contributing if the money is taken away and people preferred direct incentives and the possibility of incentives was not as strong a motivator
A Review on the Applications of Crowdsourcing in Human Pathology
The advent of the digital pathology has introduced new avenues of diagnostic
medicine. Among them, crowdsourcing has attracted researchers' attention in the
recent years, allowing them to engage thousands of untrained individuals in
research and diagnosis. While there exist several articles in this regard,
prior works have not collectively documented them. We, therefore, aim to review
the applications of crowdsourcing in human pathology in a semi-systematic
manner. We firstly, introduce a novel method to do a systematic search of the
literature. Utilizing this method, we, then, collect hundreds of articles and
screen them against a pre-defined set of criteria. Furthermore, we crowdsource
part of the screening process, to examine another potential application of
crowdsourcing. Finally, we review the selected articles and characterize the
prior uses of crowdsourcing in pathology
HIGGINS: where knowledge acquisition meets the crowds
We present HIGGINS, an engine for high quality Knowl- edge Acquisition (KA), placing special emphasis on its ar- chitecture. The distinguishing characteristic and novelty of HIGGINS lies in its special blending of two engines: An automated Information Extraction (IE) engine, aided by semantic resources, and a game-based, Human Computing engine (HC). We focus on KA from web data and text sources and, in particular, on deriving relationships between enti- ties. As a running application we utilise movie narratives, using which we wish to derive relationships among movie characters
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
Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances"
Humor is an essential human trait. Efforts to understand humor have called
out links between humor and the foundations of cognition, as well as the
importance of humor in social engagement. As such, it is a promising and
important subject of study, with relevance for artificial intelligence and
human-computer interaction. Previous computational work on humor has mostly
operated at a coarse level of granularity, e.g., predicting whether an entire
sentence, paragraph, document, etc., is humorous. As a step toward deep
understanding of humor, we seek fine-grained models of attributes that make a
given text humorous. Starting from the observation that satirical news
headlines tend to resemble serious news headlines, we build and analyze a
corpus of satirical headlines paired with nearly identical but serious
headlines. The corpus is constructed via Unfun.me, an online game that
incentivizes players to make minimal edits to satirical headlines with the goal
of making other players believe the results are serious headlines. The edit
operations used to successfully remove humor pinpoint the words and concepts
that play a key role in making the original, satirical headline funny. Our
analysis reveals that the humor tends to reside toward the end of headlines,
and primarily in noun phrases, and that most satirical headlines follow a
certain logical pattern, which we term false analogy. Overall, this paper
deepens our understanding of the syntactic and semantic structure of satirical
news headlines and provides insights for building humor-producing systems.Comment: Proceedings of the 33rd AAAI Conference on Artificial Intelligence,
201
Competing or aiming to be average?: Normification as a means of engaging digital volunteers
Engagement, motivation and active contribution by digital volunteers are key requirements for crowdsourcing and citizen science projects. Many systems use competitive elements, for example point scoring and leaderboards, to achieve these ends. However, while competition may motivate some people, it can have a neutral or demotivating effect on others. In this paper we explore theories of personal and social norms and investigate normification as an alternative approach to engagement, to be used alongside or instead of competitive strategies. We provide a systematic review of existing crowdsourcing and citizen science literature and categorise the ways that theories of norms have been incorporated to date. We then present qualitative interview data from a pro-environmental crowdsourcing study, Close the Door, which reveals normalising attitudes in certain participants. We assess how this links with competitive behaviour and participant performance. Based on our findings and analysis of norm theories, we consider the implications for designers wishing to use normification as an engagement strategy in crowdsourcing and citizen science systems
- …