25 research outputs found
What Twitter Profile and Posted Images Reveal About Depression and Anxiety
Previous work has found strong links between the choice of social media
images and users' emotions, demographics and personality traits. In this study,
we examine which attributes of profile and posted images are associated with
depression and anxiety of Twitter users. We used a sample of 28,749 Facebook
users to build a language prediction model of survey-reported depression and
anxiety, and validated it on Twitter on a sample of 887 users who had taken
anxiety and depression surveys. We then applied it to a different set of 4,132
Twitter users to impute language-based depression and anxiety labels, and
extracted interpretable features of posted and profile pictures to uncover the
associations with users' depression and anxiety, controlling for demographics.
For depression, we find that profile pictures suppress positive emotions rather
than display more negative emotions, likely because of social media
self-presentation biases. They also tend to show the single face of the user
(rather than show her in groups of friends), marking increased focus on the
self, emblematic for depression. Posted images are dominated by grayscale and
low aesthetic cohesion across a variety of image features. Profile images of
anxious users are similarly marked by grayscale and low aesthetic cohesion, but
less so than those of depressed users. Finally, we show that image features can
be used to predict depression and anxiety, and that multitask learning that
includes a joint modeling of demographics improves prediction performance.
Overall, we find that the image attributes that mark depression and anxiety
offer a rich lens into these conditions largely congruent with the
psychological literature, and that images on Twitter allow inferences about the
mental health status of users.Comment: ICWSM 201
Dataless Knowledge Fusion by Merging Weights of Language Models
Fine-tuning pre-trained language models has become the prevalent paradigm for
building downstream NLP models. Oftentimes fine-tuned models are readily
available but their training data is not, due to data privacy or intellectual
property concerns. This creates a barrier to fusing knowledge across individual
models to yield a better single model. In this paper, we study the problem of
merging individual models built on different training data sets to obtain a
single model that performs well both across all data set domains and can
generalize on out-of-domain data. We propose a dataless knowledge fusion method
that merges models in their parameter space, guided by weights that minimize
prediction differences between the merged model and the individual models. Over
a battery of evaluation settings, we show that the proposed method
significantly outperforms baselines such as Fisher-weighted averaging or model
ensembling. Further, we find that our method is a promising alternative to
multi-task learning that can preserve or sometimes improve over the individual
models without access to the training data. Finally, model merging is more
efficient than training a multi-task model, thus making it applicable to a
wider set of scenarios.Comment: ICLR 2023; The code is available at
https://github.com/bloomberg/dataless-model-mergin
Unsupervised Contrast-Consistent Ranking with Language Models
Language models contain ranking-based knowledge and are powerful solvers of
in-context ranking tasks. For instance, they may have parametric knowledge
about the ordering of countries by size or may be able to rank reviews by
sentiment. Recent work focuses on pairwise, pointwise, and listwise prompting
techniques to elicit a language model's ranking knowledge. However, we find
that even with careful calibration and constrained decoding, prompting-based
techniques may not always be self-consistent in the rankings they produce. This
motivates us to explore an alternative approach that is inspired by an
unsupervised probing method called Contrast-Consistent Search (CCS). The idea
is to train a probing model guided by a logical constraint: a model's
representation of a statement and its negation must be mapped to contrastive
true-false poles consistently across multiple statements. We hypothesize that
similar constraints apply to ranking tasks where all items are related via
consistent pairwise or listwise comparisons. To this end, we extend the binary
CCS method to Contrast-Consistent Ranking (CCR) by adapting existing ranking
methods such as the Max-Margin Loss, Triplet Loss, and Ordinal Regression
objective. Our results confirm that, for the same language model, CCR probing
outperforms prompting and even performs on a par with prompting much larger
language models
Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective
Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court’s decisions with a strong accuracy (79% on average). Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis
Whose Tweets are Surveilled for the Police: An Audit of Social-Media Monitoring Tool via Log Files
Social media monitoring by law enforcement is becoming commonplace, but
little is known about what software packages for it do. Through public records
requests, we obtained log files from the Corvallis (Oregon) Police Department's
use of social media monitoring software called DigitalStakeout. These log files
include the results of proprietary searches by DigitalStakeout that were
running over a period of 13 months and include 7240 social media posts. In this
paper, we focus on the Tweets logged in this data and consider the racial and
ethnic identity (through manual coding) of the users that are therein flagged
by DigitalStakeout. We observe differences in the demographics of the users
whose Tweets are flagged by DigitalStakeout compared to the demographics of the
Twitter users in the region, however, our sample size is too small to determine
significance. Further, the demographics of the Twitter users in the region do
not seem to reflect that of the residents of the region, with an apparent
higher representation of Black and Hispanic people. We also reconstruct the
keywords related to a Narcotics report set up by DigitalStakeout for the
Corvallis Police Department and find that these keywords flag Tweets unrelated
to narcotics or flag Tweets related to marijuana, a drug that is legal for
recreational use in Oregon. Almost all of the keywords have a common meaning
unrelated to narcotics (e.g.\ broken, snow, hop, high) that call into question
the utility that such a keyword based search could have to law enforcement.Comment: 21 Pages, 2 figures. To to be Published in FAT* 2020 Proceeding
You are what emojis say about your pictures: Language - independent gender inference attack on Facebook
International audienceThe picture owner's gender has a strong influence on individuals' emotional reactions to the picture. In this study, we investigate gender inference attacks on their owners from pictures meta-data composed of: (i) alt-texts generated by Facebook to describe the content of pictures, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we study the correlation of picture owner gender with alt-text, and Emojis/Emoticons used by commenters when reacting to these pictures. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. We show that such a privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable