The role of online attention in the supply of disinformation in
Wikipedia
- URL: http://arxiv.org/abs/2302.08576v1
- Date: Thu, 16 Feb 2023 20:44:21 GMT
- Title: The role of online attention in the supply of disinformation in
Wikipedia
- Authors: Anis Elebiary and Giovanni Luca Ciampaglia
- Abstract summary: We measure the relationship between allocation of attention and the production of hoax articles on the English Wikipedia.
Analysis of traffic logs reveals that, compared to legitimate articles created on the same day, hoaxes tend to be more associated with traffic spikes preceding their creation.
- Score: 0.030458514384586396
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Wikipedia and many User-Generated Content (UGC) communities are known for
producing reliable, quality content, but also for being vulnerable to false or
misleading information. Previous work has shown that many hoaxes on Wikipedia
go undetected for extended periods of time. But little is known about the
creation of intentionally false or misleading information online. Does
collective attention toward a topic increase the likelihood it will spawn
disinformation? Here, we measure the relationship between allocation of
attention and the production of hoax articles on the English Wikipedia.
Analysis of traffic logs reveals that, compared to legitimate articles created
on the same day, hoaxes tend to be more associated with traffic spikes
preceding their creation. This is consistent with the idea that the supply of
false or misleading information on a topic is driven by the attention it
receives. These findings improve our comprehension of the determinants of
disinformation in UGC communities and could help promote the integrity of
knowledge on Wikipedia.
Related papers
- Hoaxpedia: A Unified Wikipedia Hoax Articles Dataset [10.756673240445709]
Hoaxes are a recognised form of disinformation created deliberately, with potential serious implications in the credibility of Wikipedia.
We first provide a systematic analysis of the similarities and discrepancies between legitimate and hoax Wikipedia articles.
We then introduce Hoaxpedia, a collection of 311 Hoax articles alongside semantically similar real articles.
We report results of binary classification experiments in the task of predicting whether a Wikipedia article is real or hoax, and analyze several settings as well as a range of language models.
arXiv Detail & Related papers (2024-05-03T15:25:48Z) - Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact
Verification [80.31112722910787]
We propose Decker, a commonsense fact verification model that is capable of bridging heterogeneous knowledge.
Experimental results on two commonsense fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the effectiveness of our Decker.
arXiv Detail & Related papers (2023-05-10T06:28:16Z) - Between News and History: Identifying Networked Topics of Collective
Attention on Wikipedia [0.0]
We develop a temporal community detection approach towards topic detection.
We apply this method to a dataset of one year of current events on Wikipedia.
We are able to resolve the topics that more strongly reflect unfolding current events vs more established knowledge.
arXiv Detail & Related papers (2022-11-14T18:36:21Z) - Improving Wikipedia Verifiability with AI [116.69749668874493]
We develop a neural network based system, called Side, to identify Wikipedia citations that are unlikely to support their claims.
Our first citation recommendation collects over 60% more preferences than existing Wikipedia citations for the same top 10% most likely unverifiable claims.
Our results indicate that an AI-based system could be used, in tandem with humans, to improve the verifiability of Wikipedia.
arXiv Detail & Related papers (2022-07-08T15:23:29Z) - Adherence to Misinformation on Social Media Through Socio-Cognitive and
Group-Based Processes [79.79659145328856]
We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation.
We make the case that polarization and misinformation adherence are closely tied.
arXiv Detail & Related papers (2022-06-30T12:34:24Z) - Misinfo Belief Frames: A Case Study on Covid & Climate News [49.979419711713795]
We propose a formalism for understanding how readers perceive the reliability of news and the impact of misinformation.
We introduce the Misinfo Belief Frames (MBF) corpus, a dataset of 66k inferences over 23.5k headlines.
Our results using large-scale language modeling to predict misinformation frames show that machine-generated inferences can influence readers' trust in news headlines.
arXiv Detail & Related papers (2021-04-18T09:50:11Z) - The Role of the Crowd in Countering Misinformation: A Case Study of the
COVID-19 Infodemic [15.885290526721544]
We focus on tweets related to the COVID-19 pandemic, analyzing the spread of misinformation, professional fact checks, and the crowd response to popular misleading claims about COVID-19.
We train a classifier to create a novel dataset of 155,468 COVID-19-related tweets, containing 33,237 false claims and 33,413 refuting arguments.
We observe that the surge in misinformation tweets results in a quick response and a corresponding increase in tweets that refute such misinformation.
arXiv Detail & Related papers (2020-11-11T13:48:44Z) - Characterizing COVID-19 Misinformation Communities Using a Novel Twitter
Dataset [9.60966128833701]
We present a methodology and analyses to characterize the two competing COVID-19 misinformation communities online.
Our analyses show that COVID-19 misinformed communities are denser, and more organized than informed communities.
Our sociolinguistic analyses suggest that COVID-19 informed users tend to use more narratives than misinformed users.
arXiv Detail & Related papers (2020-08-03T11:44:22Z) - Misinformation Has High Perplexity [55.47422012881148]
We propose to leverage the perplexity to debunk false claims in an unsupervised manner.
First, we extract reliable evidence from scientific and news sources according to sentence similarity to the claims.
Second, we prime a language model with the extracted evidence and finally evaluate the correctness of given claims based on the perplexity scores at debunking time.
arXiv Detail & Related papers (2020-06-08T15:13:44Z) - Information Consumption and Social Response in a Segregated Environment:
the Case of Gab [74.5095691235917]
This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic.
We find that there are no strong statistical differences in the social response to questionable and reliable content.
Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
arXiv Detail & Related papers (2020-06-03T11:34:25Z) - Quantifying Engagement with Citations on Wikipedia [13.703047949952852]
One in 300 page views results in a reference click.
Clicks occur more frequently on shorter pages and on pages of lower quality.
Recent content, open access sources and references about life events are particularly popular.
arXiv Detail & Related papers (2020-01-23T15:52:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.