SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks

Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of training data. Instruction tuning has been shown to improve the many capabilities of large language models (LLMs) such as commonsense reasoning, reading comprehension, and computer programming. However, little is known about the effectiveness of instruction tuning on the social domain where implicit pragmatic cues are often needed to be captured. We explore the use of instruction tuning for social science NLP tasks and introduce Socialite-Llama -- an open-source, instruction-tuned Llama. On a suite of 20 social science tasks, Socialite-Llama improves upon the performance of Llama as well as matches or improves upon the performance of a state-of-the-art, multi-task finetuned model on a majority of them. Further, Socialite-Llama also leads to improvement on 5 out of 6 related social tasks as compared to Llama, suggesting instruction tuning can lead to generalized social understanding. All resources including our code, model and dataset can be found through bit.ly/socialitellama.

Historical patterns of rice farming explain modern-day language use in China and Japan more than modernization and urbanization

We used natural language processing to analyze a billion words to study cultural differences on Weibo, one of China's largest social media platforms. We compared predictions from two common explanations about cultural differences in China (economic development and urban-rural differences) against the less-obvious legacy of rice versus wheat farming. Rice farmers had to coordinate shared irrigation networks and exchange labor to cope with higher labor requirements. In contrast, wheat relied on rainfall and required half as much labor. We test whether this legacy made southern China more interdependent. Across all word categories, rice explained twice as much variance as economic development and urbanization. Rice areas used more words reflecting tight social ties, holistic thought, and a cautious, prevention orientation. We then used Twitter data comparing prefectures in Japan, which largely replicated the results from China. This provides crucial evidence of the rice theory in a different nation, language, and platform.

Human-Centered Metrics for Dialog System Evaluation

We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens: conversational agents express a diversity of both states (short-term factors like emotions) and traits (longer-term factors like personality) just as people do. These interpretable metrics consist of five measures from established psychology constructs that can be applied both across dialogs and on turns within dialogs: emotional entropy, linguistic style and emotion matching, as well as agreeableness and empathy. We compare these human metrics against 6 state-of-the-art automatic metrics (e.g. BARTScore and BLEURT) on 7 standard dialog system data sets. We also introduce a novel data set, the Three Bot Dialog Evaluation Corpus, which consists of annotated conversations from ChatGPT, GPT-3, and BlenderBot. We demonstrate the proposed human metrics offer novel information, are uncorrelated with automatic metrics, and lead to increased accuracy beyond existing automatic metrics for predicting crowd-sourced dialog judgements. The interpretability and unique signal of our proposed human-centered framework make it a valuable tool for evaluating and improving dialog systems.

Robust language-based mental health assessments in time and space through social media

Compared to physical health, population mental health measurement in the U.S. is very coarse-grained. Currently, in the largest population surveys, such as those carried out by the Centers for Disease Control or Gallup, mental health is only broadly captured through "mentally unhealthy days" or "sadness", and limited to relatively infrequent state or metropolitan estimates. Through the large scale analysis of social media data, robust estimation of population mental health is feasible at much higher resolutions, up to weekly estimates for counties. In the present work, we validate a pipeline that uses a sample of 1.2 billion Tweets from 2 million geo-located users to estimate mental health changes for the two leading mental health conditions, depression and anxiety. We find moderate to large associations between the language-based mental health assessments and survey scores from Gallup for multiple levels of granularity, down to the county-week (fixed effects $\beta = .25$ to $1.58$; $p<.001$). Language-based assessment allows for the cost-effective and scalable monitoring of population mental health at weekly time scales. Such spatially fine-grained time series are well suited to monitor effects of societal events and policies as well as enable quasi-experimental study designs in population health and other disciplines. Beyond mental health in the U.S., this method generalizes to a broad set of psychological outcomes and allows for community measurement in under-resourced settings where no traditional survey measures - but social media data - are available.

Lived Experience Matters: Automatic Detection of Stigma toward People Who Use Substances on Social Media

Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment. Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people who use substances have not. This paper explores stigma toward PWUS using a data set of roughly 5,000 public Reddit posts. We performed a crowd-sourced annotation task where workers are asked to annotate each post for the presence of stigma toward PWUS and answer a series of questions related to their experiences with substance use. Results show that workers who use substances or know someone with a substance use disorder are more likely to rate a post as stigmatizing. Building on this, we use a supervised machine learning framework that centers workers with lived substance use experience to label each Reddit post as stigmatizing. Modeling person-level demographics in addition to comment-level language results in a classification accuracy (as measured by AUC) of 0.69 -- a 17% increase over modeling language alone. Finally, we explore the linguist cues which distinguish stigmatizing content: PWUS substances and those who don't agree that language around othering ("people", "they") and terms like "addict" are stigmatizing, while PWUS (as opposed to those who do not) find discussions around specific substances more stigmatizing. Our findings offer insights into the nature of perceived stigma in substance use. Additionally, these results further establish the subjective nature of such machine learning tasks, highlighting the need for understanding their social contexts.

Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community

In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is "the asshole"). In general, first person narratives are a unique storytelling domain where the author is the narrator (the person telling the story) but can also be a character (the person living the story) and, thus, the author has two distinct voices presented in the story. In this study, we identify linguistic and narrative features associated with the author as the character or as a narrator. We use these features to answer the following questions: (1) what makes an asshole character and (2) what makes an asshole narrator? We extract both Author-as-Character features (e.g., demographics, narrative event chain, and emotional arc) and Author-as-Narrator features (i.e., the style and emotion of the story as a whole) in order to identify which aspects of the narrative are correlated with the final moral judgment. Our work shows that "assholes" as Characters frame themselves as lacking agency with a more positive personal arc, while "assholes" as Narrators will tell emotional and opinionated stories.

Cross-Platform Difference in Facebook and Text Messages Language Use: Illustrated by Depression Diagnosis

How does language differ across one's Facebook status updates vs. one's text messages (SMS)? In this study, we show how Facebook and SMS use differs in psycho-linguistic characteristics and how these differences drive downstream analyses with an illustration of depression diagnosis. We use a sample of consenting participants who shared Facebook status updates, SMS data, and answered a standard psychological depression screener. We quantify domain differences using psychologically driven lexical methods and find that language on Facebook involves more personal concerns, experiences, and content features while the language in SMS contains more informal and style features. Next, we estimate depression from both text domains, using a depression model trained on Facebook data, and find a drop in accuracy when predicting self-reported depression assessments from the SMS-based depression estimates. Finally, we evaluate a simple domain adaption correction based on words driving the cross-platform differences and applied it to the SMS-derived depression estimates, resulting in significant improvement in prediction. Our work shows the Facebook vs. SMS difference in language use and suggests the necessity of cross-domain adaption for text-based predictions.

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., 'pleasant' and 'unpleasant' words). We show that anti-black WEAT estimates from geo-tagged social media data at the level of metropolitan statistical areas strongly correlate with several measures of racial animus--even when controlling for sociodemographic covariates. However, we also show that every one of these correlations is explained by a third variable: the frequency of Black names in the underlying corpora relative to White names. This occurs because word embeddings tend to group positive (negative) words and frequent (rare) words together in the estimated semantic space. As the frequency of Black names on social media is strongly correlated with Black Americans' prevalence in the population, this results in spurious anti-Black WEAT estimates wherever few Black Americans live. This suggests that research using the WEAT to measure bias should consider term frequency, and also demonstrates the potential consequences of using black-box models like word embeddings to study human cognition and behavior.

Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2020

Black Lives Matter (BLM) is a grassroots movement protesting violence towards Black individuals and communities with a focus on police brutality. The movement has gained significant media and political attention following the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd and the shooting of Jacob Blake in 2020. Due to its decentralized nature, the #BlackLivesMatter social media hashtag has come to both represent the movement and been used as a call to action. Similar hashtags have appeared to counter the BLM movement, such as #AllLivesMatter and #BlueLivesMatter. We introduce a data set of 41.8 million tweets from 10 million users which contain one of the following keywords: BlackLivesMatter, AllLivesMatter and BlueLivesMatter. This data set contains all currently available tweets from the beginning of the BLM movement in 2013 to June 2020. We summarize the data set and show temporal trends in use of both the BlackLivesMatter keyword and keywords associated with counter movements. In the past, similarly themed, though much smaller in scope, BLM data sets have been used for studying discourse in protest and counter protest movements, predicting retweets, examining the role of social media in protest movements and exploring narrative agency. This paper open-sources a large-scale data set to facilitate research in the areas of computational social science, communications, political science, natural language processing, and machine learning.