1,091 research outputs found
Modeling Global Syntactic Variation in English Using Dialect Classification
This paper evaluates global-scale dialect identification for 14 national
varieties of English as a means for studying syntactic variation. The paper
makes three main contributions: (i) introducing data-driven language mapping as
a method for selecting the inventory of national varieties to include in the
task; (ii) producing a large and dynamic set of syntactic features using
grammar induction rather than focusing on a few hand-selected features such as
function words; and (iii) comparing models across both web corpora and social
media corpora in order to measure the robustness of syntactic variation across
registers
Inferring the Origin Locations of Tweets with Quantitative Confidence
Social Internet content plays an increasingly critical role in many domains,
including public health, disaster management, and politics. However, its
utility is limited by missing geographic information; for example, fewer than
1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable,
content-based approach to estimate the location of tweets using a novel yet
simple variant of gaussian mixture models. Further, because real-world
applications depend on quantified uncertainty for such estimates, we propose
novel metrics of accuracy, precision, and calibration, and we evaluate our
approach accordingly. Experiments on 13 million global, comprehensively
multi-lingual tweets show that our approach yields reliable, well-calibrated
results competitive with previous computationally intensive methods. We also
show that a relatively small number of training data are required for good
estimates (roughly 30,000 tweets) and models are quite time-invariant
(effective on tweets many weeks newer than the training set). Finally, we show
that toponyms and languages with small geographic footprint provide the most
useful location signals.Comment: 14 pages, 6 figures. Version 2: Move mathematics to appendix, 2 new
references, various other presentation improvements. Version 3: Various
presentation improvements, accepted at ACM CSCW 201
Who let the trolls out? Towards understanding state-sponsored trolls
Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused ?trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's in- formation ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence detection is not straightforward. Using Hawkes Processes, we quantify the influence these accounts have on pushing URLs on four platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab. In general, Russian trolls were more influential and efficient in pushing URLs to all the other platforms with the exception of /pol/ where Iranians were more influential. Finally, we release our source code to ensure the reproducibility of our results and to encourage other researchers to work on understanding other emerging kinds of state-sponsored troll accounts on Twitter.https://arxiv.org/pdf/1811.03130.pdfAccepted manuscrip
Multilingual Cross-domain Perspectives on Online Hate Speech
In this report, we present a study of eight corpora of online hate speech, by
demonstrating the NLP techniques that we used to collect and analyze the
jihadist, extremist, racist, and sexist content. Analysis of the multilingual
corpora shows that the different contexts share certain characteristics in
their hateful rhetoric. To expose the main features, we have focused on text
classification, text profiling, keyword and collocation extraction, along with
manual annotation and qualitative study.Comment: 24 page
Alleviating data sparsity for Twitter sentiment analysis
Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit. In this work we propose using two different sets of features to alleviate the data sparseness problem. One is the semantic feature set where we extract semantically hidden concepts from tweets and then incorporate them into classifier training through interpolation. Another is the sentiment-topic feature set where we extract latent topics and the associated topic sentiment from tweets, then augment the original feature space with these sentiment-topics. Experimental results on the Stanford Twitter Sentiment Dataset show that both feature sets outperform the baseline model using unigrams only. Moreover, using semantic features rivals the previously reported best result. Using sentiment topic features achieves 86.3% sentiment classification accuracy, which outperforms existing approaches
- …