24 research outputs found
Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping
We propose Quootstrap, a method for extracting quotations, as well as the
names of the speakers who uttered them, from large news corpora. Whereas prior
work has addressed this problem primarily with supervised machine learning, our
approach follows a fully unsupervised bootstrapping paradigm. It leverages the
redundancy present in large news corpora, more precisely, the fact that the
same quotation often appears across multiple news articles in slightly
different contexts. Starting from a few seed patterns, such as ["Q", said S.],
our method extracts a set of quotation-speaker pairs (Q, S), which are in turn
used for discovering new patterns expressing the same quotations; the process
is then repeated with the larger pattern set. Our algorithm is highly scalable,
which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus.
Validating our results against a crowdsourced ground truth, we obtain 90%
precision at 40% recall using a single seed pattern, with significantly higher
recall values for more frequently reported (and thus likely more interesting)
quotations. Finally, we showcase the usefulness of our algorithm's output for
computational social science by analyzing the sentiment expressed in our
extracted quotations.Comment: Accepted at the 12th International Conference on Web and Social Media
(ICWSM), 201
Structuring Wikipedia Articles with Section Recommendations
Sections are the building blocks of Wikipedia articles. They enhance
readability and can be used as a structured entry point for creating and
expanding articles. Structuring a new or already existing Wikipedia article
with sections is a hard task for humans, especially for newcomers or less
experienced editors, as it requires significant knowledge about how a
well-written article looks for each possible topic. Inspired by this need, the
present paper defines the problem of section recommendation for Wikipedia
articles and proposes several approaches for tackling it. Our systems can help
editors by recommending what sections to add to already existing or newly
created Wikipedia articles. Our basic paradigm is to generate recommendations
by sourcing sections from articles that are similar to the input article. We
explore several ways of defining similarity for this purpose (based on topic
modeling, collaborative filtering, and Wikipedia's category system). We use
both automatic and human evaluation approaches for assessing the performance of
our recommendation system, concluding that the category-based approach works
best, achieving precision@10 of about 80% in the human evaluation.Comment: SIGIR '18 camera-read
CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations
Recent work has aimed to capture nuances of human behavior by using LLMs to
simulate responses from particular demographics in settings like social science
experiments and public opinion surveys. However, there are currently no
established ways to discuss or evaluate the quality of such LLM simulations.
Moreover, there is growing concern that these LLM simulations are flattened
caricatures of the personas that they aim to simulate, failing to capture the
multidimensionality of people and perpetuating stereotypes. To bridge these
gaps, we present CoMPosT, a framework to characterize LLM simulations using
four dimensions: Context, Model, Persona, and Topic. We use this framework to
measure open-ended LLM simulations' susceptibility to caricature, defined via
two criteria: individuation and exaggeration. We evaluate the level of
caricature in scenarios from existing work on LLM simulations. We find that for
GPT-4, simulations of certain demographics (political and marginalized groups)
and topics (general, uncontroversial) are highly susceptible to caricature.Comment: To appear at EMNLP 2023 (Main
AnthroScore: A Computational Linguistic Measure of Anthropomorphism
Anthropomorphism, or the attribution of human-like characteristics to
non-human entities, has shaped conversations about the impacts and
possibilities of technology. We present AnthroScore, an automatic metric of
implicit anthropomorphism in language. We use a masked language model to
quantify how non-human entities are implicitly framed as human by the
surrounding context. We show that AnthroScore corresponds with human judgments
of anthropomorphism and dimensions of anthropomorphism described in social
science literature. Motivated by concerns of misleading anthropomorphism in
computer science discourse, we use AnthroScore to analyze 15 years of research
papers and downstream news articles. In research papers, we find that
anthropomorphism has steadily increased over time, and that papers related to
language models have the most anthropomorphism. Within ACL papers, temporal
increases in anthropomorphism are correlated with key neural advancements.
Building upon concerns of scientific misinformation in mass media, we identify
higher levels of anthropomorphism in news headlines compared to the research
papers they cite. Since AnthroScore is lexicon-free, it can be directly applied
to a wide range of text sources.Comment: EACL 2024 Main Conferenc
On the Value of Wikipedia as a Gateway to the Web
By linking to external websites, Wikipedia can act as a gateway to the Web.
To date, however, little is known about the amount of traffic generated by
Wikipedia's external links. We fill this gap in a detailed analysis of usage
logs gathered from Wikipedia users' client devices. Our analysis proceeds in
three steps: First, we quantify the level of engagement with external links,
finding that, in one month, English Wikipedia generated 43M clicks to external
websites, in roughly even parts via links in infoboxes, cited references, and
article bodies. Official links listed in infoboxes have by far the highest
click-through rate (CTR), 2.47% on average. In particular, official links
associated with articles about businesses, educational institutions, and
websites have the highest CTR, whereas official links associated with articles
about geographical content, television, and music have the lowest CTR. Second,
we investigate patterns of engagement with external links, finding that
Wikipedia frequently serves as a stepping stone between search engines and
third-party websites, effectively fulfilling information needs that search
engines do not meet. Third, we quantify the hypothetical economic value of the
clicks received by external websites from English Wikipedia, by estimating that
the respective website owners would need to pay a total of $7--13 million per
month to obtain the same volume of traffic via sponsored search. Overall, these
findings shed light on Wikipedia's role not only as an important source of
information, but also as a high-traffic gateway to the broader Web ecosystem.Comment: The Web Conference WWW 2021, 12 page
In-class Data Analysis Replications: Teaching Students while Testing Science
Science is facing a reproducibility crisis. Previous work has proposed
incorporating data analysis replications into classrooms as a potential
solution. However, despite the potential benefits, it is unclear whether this
approach is feasible, and if so, what the involved stakeholders-students,
educators, and scientists-should expect from it. Can students perform a data
analysis replication over the course of a class? What are the costs and
benefits for educators? And how can this solution help benchmark and improve
the state of science?
In the present study, we incorporated data analysis replications in the
project component of the Applied Data Analysis course (CS-401) taught at EPFL
(N=354 students). Here we report pre-registered findings based on surveys
administered throughout the course. First, we demonstrate that students can
replicate previously published scientific papers, most of them qualitatively
and some exactly. We find discrepancies between what students expect of data
analysis replications and what they experience by doing them along with changes
in expectations about reproducibility, which together serve as evidence of
attitude shifts to foster students' critical thinking. Second, we provide
information for educators about how much overhead is needed to incorporate
replications into the classroom and identify concerns that replications bring
as compared to more traditional assignments. Third, we identify tangible
benefits of the in-class data analysis replications for scientific communities,
such as a collection of replication reports and insights about replication
barriers in scientific work that should be avoided going forward.
Overall, we demonstrate that incorporating replication tasks into a large
data science class can increase the reproducibility of scientific work as a
by-product of data science instruction, thus benefiting both science and
students
ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΡΠ°Π±ΠΎΡΡ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ Π² ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ. (Π§Π°ΡΡΡ 4)
Wikipedia is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia was not conceived as a source of original information, but as a gateway to secondary sources: according to Wikipediaβs guidelines, facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the heart of Wikipedia, little is known about how users interact with them. To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first anal-ysis of readersβ interactions with citations. We find that overall engagement with citations is low: about one in 300 page views results in a reference click (0.29% overall; 0.56% on desktop; 0.13% on mobile). Matched observational studies of the factors associated with reference clicking reveal that clicks occur more frequently on shorter pages and on pages of lower quality, suggesting that references are consulted more commonly when Wikipedia itself does not contain the information sought by the user. Moreover, we observe that recent content, open access sources, and references about life events (births, deaths, marriages, etc.) are particularly popular. Taken together, our findings deepen our understanding of Wikipediaβs role in a global information economy where reliability is ever less certain, and source attribution ever more vital.ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ β ΠΎΠ΄ΠΈΠ½ ΠΈΠ· ΡΠ°ΠΌΡΡ
ΠΏΠΎΡΠ΅ΡΠ°Π΅ΠΌΡΡ
ΡΠ°ΠΉΡΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠ° ΠΈ ΡΠ°ΠΌΡΡ
ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΠ½Π½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄Π»Ρ ΠΌΠ½ΠΎΠ³ΠΈΡ
ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΠ½ΡΠΈΠΊΠ»ΠΎΠΏΠ΅Π΄ΠΈΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ Π·Π°Π΄ΡΠΌΡΠ²Π°Π»Π°ΡΡ Π½Π΅ ΠΊΠ°ΠΊ ΠΈΡΡΠΎΡΠ½ΠΈΠΊ ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»ΡΠ½ΠΎΠΉ (ΠΎΠΊΠΎΠ½ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠΉ) Π½Π°ΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, Π°, ΡΠΊΠΎΡΠ΅Π΅, ΠΊΠ°ΠΊ Π²ΠΎΡΠΎΡΠ° ΠΊ Π±ΠΎΠ»Π΅Π΅ Π³Π»ΡΠ±ΠΎΠΊΠΈΠΌ ΠΈ ΡΠΎΡΠ½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌ. Π ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π±Π°Π·ΠΎΠ²ΡΠΌΠΈ ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°ΠΌΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ ΡΠ°ΠΊΡΡ Π΄ΠΎΠ»ΠΆΠ½Ρ Π±ΡΡΡ ΠΏΠΎΠ΄ΠΊΡΠ΅ΠΏΠ»Π΅Π½Ρ Π½Π°Π΄ΡΠΆΠ½ΡΠΌΠΈ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΡΠ°ΠΆΠ°ΡΡ ΠΏΠΎΠ»Π½ΡΠΉ ΡΠΏΠ΅ΠΊΡΡ Π²ΡΠ΅Ρ
ΠΌΠ½Π΅Π½ΠΈΠΉ ΠΏΠΎ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΠΎΠΉ ΡΠ΅ΠΌΠ΅. Π₯ΠΎΡΡ ΡΠΈΡΠ°ΡΡ Π»Π΅ΠΆΠ°Ρ Π² ΠΎΡΠ½ΠΎΠ²Π΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ, ΠΏΠΎΠΊΠ° ΠΌΠ°Π»ΠΎ ΡΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΠΎ ΠΎ ΡΠΎΠΌ, ΠΊΠ°ΠΊ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΠΈ ΡΠ°Π±ΠΎΡΠ°ΡΡ Ρ Π½ΠΈΠΌΠΈ. Π§ΡΠΎΠ±Ρ ΡΡΡΡΠ°Π½ΠΈΡΡ ΡΡΠΎΡ ΠΏΡΠΎΠ±Π΅Π», Π°Π²ΡΠΎΡΡ ΡΠΎΠ·Π΄Π°Π»ΠΈ ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΈΠ΅ (ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅) ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ Π΄Π»Ρ Π²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΠΏΠΈΡΠ΅ΠΉ (ΠΆΡΡΠ½Π°Π»ΠΎΠ²) Π²ΡΠ΅Ρ
Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΉ ΡΠΎ ΡΡΡΠ»ΠΊΠ°ΠΌΠΈ ΠΈΠ· Π°Π½Π³Π»ΠΎΡΠ·ΡΡΠ½ΡΡ
ΡΡΠ°ΡΠ΅ΠΉ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ Π½Π° ΡΠΈΡΠΈΡΡΠ΅ΠΌΡΠ΅ ΡΡΡΠ»ΠΊΠΈ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΡΠ° ΠΈ ΠΏΡΠΎΠ²Π΅Π»ΠΈ ΠΏΠ΅ΡΠ²ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΡΠΈΡΠ°ΡΠ΅Π»Π΅ΠΉ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ. Π‘ΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π°ΠΌΠΈ ΠΏΠΎ ΡΡΡΠ»ΠΊΠ΅, ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Ρ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΡΡ ΡΠ°ΡΠ΅ Π½Π° ΡΡΡΠ°Π½ΠΈΡΠ°Ρ
Π±ΠΎΠ»Π΅Π΅ ΠΊΠΎΡΠΎΡΠΊΠΈΡ
ΠΈ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π½ΠΈΠ·- ΠΊΠΎΠ³ΠΎ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°, ΠΈΠ· ΡΠ΅Π³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠΈΡΡ, ΡΡΠΎ ΡΡΡΠ»ΠΊΠΈ ΡΠ°ΡΠ΅ Π²ΡΠ΅Π³ΠΎ ΡΡΠ΅Π±ΡΡΡΡΡ, ΠΊΠΎΠ³Π΄Π° ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ Π½Π΅ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ, ΠΊΠΎΡΠΎΡΡΡ ΠΈΡΠ΅Ρ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, Π°Π²ΡΠΎΡΡ ΠΎΠ±ΡΠ°ΡΠΈΠ»ΠΈ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅, ΡΡΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ ΠΎΡΠΊΡΡΡΠΎΠ³ΠΎ Π΄ΠΎΡΡΡΠΏΠ° ΠΈ ΡΡΡΠ»ΠΊΠΈ ΠΎ Π±ΠΈΠ±Π»ΠΈΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
(ΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ, ΡΠΌΠ΅ΡΡΠΈ, Π±ΡΠ°ΠΊΠΈ ΠΈ Ρ.Π΄.) ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ ΠΏΠΎΠΏΡΠ»ΡΡΠ½Ρ.Π‘ΠΎΠ±ΡΠ°Π½Π½ΡΠ΅ Π²ΠΎΠ΅Π΄ΠΈΠ½ΠΎ, Π½Π°ΡΠΈ Π²ΡΠ²ΠΎΠ΄Ρ ΡΠ³Π»ΡΠ±Π»ΡΡΡ ΠΏΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠΎΠ»ΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ Π² Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅, Π³Π΄Π΅ Π½Π°Π΄ΡΠΆΠ½ΠΎΡΡΡ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π²ΡΡ ΠΌΠ΅Π½Π΅Π΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ½Π½ΠΎΠΉ, Π° Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² β Π²ΡΡ Π±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½ΡΠΌ.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅, ΠΏΡΠΎΠ²Π΅Π΄ΡΠ½Π½ΠΎΠ΅ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ ΠΈΠΌΠΈ ΠΊΠ°ΠΊ Π΄ΠΎΠΊΠ»Π°Π΄ Π½Π° ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ Π² Π’Π°ΠΉΠ±ΡΠ΅ (Π’Π°ΠΉΠ²Π°Π½Ρ) Π² Π°ΠΏΡΠ΅Π»Π΅ 2020 Π³. ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΎ Π² ΡΠΈΡΡΠ΅ΠΌΠ΅ Arxive ΠΠΎΡΠ½Π΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° (Π‘Π¨Π) ΠΏΠΎΠ΄ Π»ΠΈΡΠ΅Π½Π·ΠΈΠ΅ΠΉ Creative Commons Attribution 4.0 International (cc-BY 4.0).Π‘ΠΏΡΠ°Π²ΠΎΡΠ½ΡΠΉ ΡΠΎΡΠΌΠ°Ρ ΠΠ‘Π Π΄Π»Ρ ΡΡΡΠ»ΠΎΠΊ:Π’ΠΈΡΠΈΠ°Π½ΠΎ ΠΠΈΠΊΠ°ΡΠ΄ΠΈ, ΠΠΈΡΠΈΠ°ΠΌ Π Π΅Π΄ΠΈ, ΠΠΆΠΎΠ²Π°Π½ΠΈ ΠΠΎΠ»Π°Π²ΠΈΡΡΠ° ΠΈ Π ΠΎΠ±Π΅ΡΡ ΠΠ΅ΡΡ, 2020.ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ Π² ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ. Π ΡΡΡΠ΄Π°Ρ
: ΠΠ΅Π±-ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΡ 2020 (WWWβ20), 20β24 Π°ΠΏΡ. 2020 Π³., Π’Π°ΠΉΠ±ΡΠΉ, Π’Π°ΠΉΠ²Π°Π½Ρ. ΠΠ‘Π, ΠΡΡ-ΠΠΎΡΠΊ, Π‘Π¨Π. 12 Ρ.; https://doi.org/10/1145/3366423.3380300
ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΡΠ°Π±ΠΎΡΡ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ Π² ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ. (Π§Π°ΡΡΡ 3)
Wikipedia is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia was not conceived as a source of original information, but as a gateway to secondary sources: according to Wikipediaβs guidelines, facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the heart of Wikipedia, little is known about how users interact with them. To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first analysis of readersβ interactions with citations. We find that overall engagement with citations is low: about one in 300 page views results in a reference click (0,29% overall; 0,56% on desktop; 0,13% on mobile). Matched observational studies of the factors associated with reference clicking reveal that clicks occur more frequently on shorter pages and on pages of lower quality, suggesting that references are consulted more commonly when Wikipedia itself does not contain the information sought by the user. Moreover, we observe that recent content, open access sources, and references about life events (births, deaths, marriages, etc.) are particularly popular. Taken together, our findings deepen our understanding of Wikipediaβs role in a global information economy where reliability is ever less certain, and source attribution ever more vital.ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΡΠ°ΠΌΡΡ
ΠΏΠΎΡΠ΅ΡΠ°Π΅ΠΌΡΡ
ΡΠ°ΠΉΡΠΎΠ² Π² ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠ΅ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΠ½Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄Π»Ρ ΠΌΠ½ΠΎΠ³ΠΈΡ
ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΠ½ΡΠΈΠΊΠ»ΠΎΠΏΠ΅Π΄ΠΈΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ Π·Π°Π΄ΡΠΌΡΠ²Π°Π»Π°ΡΡ Π½Π΅ ΠΊΠ°ΠΊ ΠΈΡΡΠΎΡΠ½ΠΈΠΊ ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»ΡΠ½ΠΎΠΉ (ΠΎΠΊΠΎΠ½ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠΉ) Π½Π°ΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, Π°, ΡΠΊΠΎΡΠ΅Π΅, ΠΊΠ°ΠΊ Π²ΠΎΡΠΎΡΠ° ΠΊ Π±ΠΎΠ»Π΅Π΅ Π³Π»ΡΠ±ΠΎΠΊΠΈΠΌ ΠΈ ΡΠΎΡΠ½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌ. Π ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π±Π°Π·ΠΎΠ²ΡΠΌΠΈ ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°ΠΌΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ ΡΠ°ΠΊΡΡ Π΄ΠΎΠ»ΠΆΠ½Ρ Π±ΡΡΡ ΠΏΠΎΠ΄ΠΊΡΠ΅ΠΏΠ»Π΅Π½Ρ Π½Π°Π΄ΡΠΆΠ½ΡΠΌΠΈ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΡΠ°ΠΆΠ°ΡΡ ΠΏΠΎΠ»Π½ΡΠΉ ΡΠΏΠ΅ΠΊΡΡ Π²ΡΠ΅Ρ
ΠΌ Π½Π΅Π½ΠΈΠΉ ΠΏΠΎ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠ΅. Π₯ΠΎΡΡ ΡΠΈΡΠ°ΡΡ Π»Π΅ΠΆΠ°Ρ Π² ΠΎΡΠ½ΠΎΠ²Π΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ, ΠΏΠΎΠΊΠ° ΠΌΠ°Π»ΠΎ ΡΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΠΎ ΠΎ ΡΠΎΠΌ, ΠΊΠ°ΠΊ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΠΈ ΡΠ°Π±ΠΎΡΠ°ΡΡ Ρ Π½ΠΈΠΌΠΈ. Π§ΡΠΎΠ±Ρ Π·Π°ΠΊΡΡΡΡ ΡΡΠΎΡ ΠΏΡΠΎΠ±Π΅Π», ΠΌΡ ΡΠΎΠ·Π΄Π°Π»ΠΈ ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΈΠ΅ (ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅) ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ Π΄Π»Ρ Π²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΠΏΠΈΡΠ΅ΠΉ (ΠΆΡΡΠ½Π°Π»ΠΎΠ²) Π²ΡΠ΅Ρ
Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΉ ΡΠΎ ΡΡΡΠ»ΠΊΠ°ΠΌΠΈ, ΠΈΠ΄ΡΡΠΈΠΌΠΈ ΠΈΠ· Π°Π½Π³Π»ΠΎΡΠ·ΡΡΠ½ΡΡ
ΡΡΠ°ΡΠ΅ΠΉ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ Π½Π° ΡΠΈΡΠΈΡΡΠ΅ΠΌΡΠ΅ ΡΡΡΠ»ΠΊΠΈ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΡΠ°, ΠΈ ΠΏΡΠΎΠ²Π΅Π»ΠΈ ΠΏΠ΅ΡΠ²ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΡΠΈΡΠ°ΡΠ΅Π»Π΅ΠΉ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ, ΡΡΠΎ Π² ΡΠ΅Π»ΠΎΠΌ Π²ΠΎΠ²Π»Π΅ΡΡΠ½Π½ΠΎΡΡΡ Π² ΡΠΈΡΠ°ΡΡ Π½ΠΈΠ·ΠΊΠ°Ρ. ΠΠΊΠΎΠ»ΠΎ 300 ΠΏΡΠΎΡΠΌΠΎΡΡΠΎΠ² ΡΡΡΠ°Π½ΠΈΡ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡ ΠΊ Π²Ρ
ΠΎΠ΄Ρ Π½Π° ΠΎΠ΄Π½Ρ ΡΡΡΠ»ΠΊΡ β ΡΡΠΎ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ Π²ΡΠ΅Π³ΠΎ 0,29%; Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ 0 ,56% ΠΏΡΠΈ ΡΠ°Π±ΠΎΡΠ΅ Ρ Π½Π°ΡΡΠΎΠ»ΡΠ½ΡΠΌ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠΎΠΌ (Π½Π° ΡΠ°Π±ΠΎΡΠ΅ΠΌ ΡΡΠΎΠ»Π΅) ΠΈ 0,13% ΠΏΡΠΈ ΡΠ°Π±ΠΎΡΠ΅ Π½Π° ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΡΡ
ΡΡΡΡΠΎΠΉΡΡΠ²Π°Ρ
. Π‘ΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π°ΠΌΠΈ ΠΏΠΎ ΡΡΡΠ»ΠΊΠ΅, ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Ρ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΡΡ ΡΠ°ΡΠ΅ Π½Π° Π±ΠΎΠ»Π΅Π΅ ΠΊΠΎΡΠΎΡΠΊΠΈΡ
ΡΡΡΠ°Π½ΠΈΡΠ°Ρ
ΠΈ Π½Π° ΡΡΡΠ°Π½ΠΈΡΠ°Ρ
ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π½ΠΈΠ·ΠΊΠΎΠ³ΠΎ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°. ΠΡΡ
ΠΎΠ΄Ρ ΠΈΠ· ΡΡΠΎΠ³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠΈΡΡ, ΡΡΠΎ ΡΡΡΠ»ΠΊΠΈ ΡΠ°ΡΠ΅ Π²ΡΠ΅Π³ΠΎ ΡΡΠ΅Π±ΡΡΡΡΡ, ΠΊΠΎΠ³Π΄Π° ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ Π½Π΅ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ, ΠΊΠΎΡΠΎΡΡΡ ΠΈΡΠ΅Ρ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΠΌΡ ΠΎΠ±ΡΠ°ΡΠΈΠ»ΠΈ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅, ΡΡΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ ΠΎΡΠΊΡΡΡΠΎΠ³ΠΎ Π΄ΠΎΡΡΡΠΏΠ° ΠΈ ΡΡΡΠ»ΠΊΠΈ ΠΎ ΠΆΠΈΠ·Π½Π΅Π½Π½ΡΡ
ΡΠΎΠ±ΡΡΠΈΡΡ
(ΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ, ΡΠΌΠ΅ΡΡΠΈ, Π±ΡΠ°ΠΊΠΈ ΠΈ Ρ.Π΄.) ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ ΠΏΠΎΠΏΡΠ»ΡΡΠ½Ρ. Π‘ΠΎΠ±ΡΠ°Π½Π½ΡΠ΅ Π²ΠΎΠ΅Π΄ΠΈΠ½ΠΎ, Π½Π°ΡΠΈ Π²ΡΠ²ΠΎΠ΄Ρ ΡΠ³Π»ΡΠ±Π»ΡΡΡ ΠΏΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠΎΠ»ΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ Π² Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅, Π³Π΄Π΅ Π½Π°Π΄ΡΠΆΠ½ΠΎΡΡΡ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π²ΡΡ ΠΌΠ΅Π½Π΅Π΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ½Π½ΠΎΠΉ, Π° Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π²ΡΡ Π±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½ΡΠΌ.Π‘ΠΏΡΠ°Π²ΠΎΡΠ½ΡΠΉ ΡΠΎΡΠΌΠ°Ρ ACM Π΄Π»Ρ ΡΡΡΠ»ΠΎΠΊ:Π’ΠΈΡΠΈΠ°Π½ΠΎ ΠΠΈΠΊΠ°ΡΠ΄ΠΈ, ΠΠΈΡΠΈΠ°ΠΌ Π Π΅Π΄ΠΈ, ΠΠΆΠΎΠ²Π°Π½Π½ΠΈ ΠΠΎΠ»Π°Π²ΠΈΡΡΠ° ΠΈ Π ΠΎΠ±Π΅ΡΡ ΠΠ΅ΡΡ. 2020.ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ Π² ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ. Π ΡΡΡΠ΄Π°Ρ
: ΠΠ΅Π±-ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΡ 2020 (WWWβ20), 20β24 Π°ΠΏΡΠ΅Π»Ρ 2020 Π³., Π’Π°ΠΉΠ±ΡΠΉ, Π’Π°ΠΉΠ²Π°Π½Ρ. ACM, ΠΡΡ-ΠΠΎΡΠΊ, ΡΡΠ°Ρ ΠΡΡ-ΠΠΎΡΠΊ, Π‘Π¨Π. 12 Ρ. https://doi.org/10.1145/3366423.3380300