Association for the Advancement of Artificial Intelligence: AAAI Publications
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Dynamics of Language Change: A Mixed-Methods Analysis of Language in an Online Transgender Community
No full textTrans-affirming language can be critical for trans people in transphobic sociopolitical contexts. But what kinds of trans activism -- and whose -- has determined what language is considered trans-affirming? Collective negotiations of trans language norms have long occurred across online platforms, providing rich data to explore what language change looks like in a community where language is highly politicized. In this paper, we use a mixed-methods approach to explore patterns of language change in one popular early online transmasculine community on LiveJournal. Using bigram snapshot language models, we establish canonical patterns of community-level change in aggregate language usage. We dig into how these patterns relate to population turnover by analyzing distinct groups of users, extending existing quantitative analysis to disentangle group-level variation from community-level change and combining it with qualitative analysis that scrutinizes the interactional contexts of change. We find influence of linguistic, interactional, and identity norms that are negotiated and enforced by highly active users, thus driving language change. Our analysis demonstrates the role of power in community-internal language negotiation, with implications for trans language change more broadly
Disruptions in Music Listening Behaviors During Lockdowns
No full textThis study examines how individual music listening behaviors evolved during the COVID-19 lockdowns in France, focusing on both listening volumes and rhythms. We combine passively collected individual listening history data, provided by a music streaming service and covering the 2019-2023 period, with survey data collected from the same users (n ≈ 10000). Using the Dynamic Time Warping method, we develop a typology of listening trajectories during the first lockdown. The results reveal significant and heterogeneous changes in listening behavior, with approximately one-third of respondents experiencing a significant decrease in listening volume, while a quarter experienced an increase. We then analyze the evolution of the intervals between consecutive music listening sessions — so called inter-session times — to assess disruptions in individual listening rhythms. We uncover an unprecedented shift in the listening rhythms at the onset of first lockdown, reflecting varying degrees of disruption in daily life rhythms. For half of the individuals this disruption lasted more than four weeks. Finally we show that age, educational attainment and household structure unevenly influence the reorganization of music listening activity during this period, shedding light on the social differentiations at work in the reorganization of an ordinary activity during this crisis period
Analyzing Reddit Stories of Sexual Violence: Incidents, Effects, and Requests for Advice
No full textWarning: This paper may contain triggering language for some readers, especially survivors of sexual violence.
Survivors of sexual violence sometimes share their experiences on social media, revealing their feelings and emotions and seeking advice. On platforms such as Reddit, some stories can be long---up to 40,000 characters. We posit that such long stories are demanding for helpers to read and respond to.
Prior research has indicated that parts of these stories describing the incident, the effects on the poster, and advice requested by the poster are important.
Highlighting those parts can draw helpers' attention toward key information and assist them in reading and responding to long stories.
We first examine the stories posted on Reddit for the prevalence of these parts. Second, we develop a computational model to highlight these parts of a story. On ten-fold cross-validation of a dataset, our model achieves a macro F1 score of 0.82. In addition, we contribute METHREE, a dataset comprising 8,947 labeled sentences for these parts from Reddit stories.
A survey of users who are helpers on some relevant subreddits shows that the parts highlighted by our tool represent important information and assist them while reading and responding to long stories. We find that these tool-generated highlights statistically significantly reduce the demandingness of long stories. Moreover, almost all helpers felt that highlighted stories are helpful and easier to read, understand, and respond to than nonhighlighted ones. In particular, on a 4-point Likert scale, there is about 0.7 point reduction in demandingess when stories were presented with highlights
Contrastive Instruction Fine-Tuning Large Multimodal Model for Hateful Meme Classification
No full textDetecting hateful memes requires a model that possesses extensive background knowledge and robust reasoning abilities, especially when the memes contain ambiguous descriptions. Previous research has used large language models (LLMs) and large multimodal models (LMMs) to interpret and categorize these memes. However, distinguishing subtly different hateful and non-hateful memes is still challenging. In recognition of this, our study introduces a unique contrastive instruction fine-tuning approach, InstructMemeCL. This method improves an LMM's ability to discern between memes that have similar visual or textual elements by intensifying its focus on semantic subtleties that separate hateful from non-hateful content. We evaluated our model using AUROC and accuracy metrics on three publicly available hateful meme datasets. The results indicate that our improved LMM more accurately identifies hateful and non-hateful memes, demonstrating superior performance compared to conventional LLMs and LMMs used in similar tasks
What’s in a Label? Propaganda Labels and User Sharing Behavior on Social Media Platforms
No full textAuthentic information is vital for a society's ability to make rational decisions. Fabricated and manipulative information can be harmful to society as seen in cases of threatening events that were consequences of foreign propaganda and radical ideologies. While past research has studied dis- and misinformation on social media platforms, the study of propaganda has received much less attention. This study explores the sharing intentions of propaganda on social media platforms and develops an intervention to help detect it. In a randomized controlled trial setting, we added indicators to social media posts that used propaganda techniques to advance an agenda, including techniques that rely on fallacious reasoning, emotional rather than logical reasoning, etc. We then asked our participants (n=1,187) about their intention to engage with these posts. We found that participants were significantly (2.4 times) less likely to share these posts with indicators. We also found that participants’ political affiliation moderated their sharing intentions. We believe our findings provide valuable insights for the study of propaganda on social media platforms
LLM-Based Semantic Augmentation for Harmful Content Detection
No full textRecent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings. However, their efficacy declines when tackling complex social media challenges such as propaganda detection, hateful meme classification, and toxicity identification. Much of the existing work has focused on using LLMs to generate synthetic training data, overlooking the potential of LLM-based text preprocessing and semantic augmentation. In this paper, we introduce an approach that prompts LLMs to clean noisy text and provide context-rich explanations, thereby enhancing training sets without substantial increases in data volume.
We systematically evaluate on the SemEval 2024 multi-label Persuasive Meme dataset and further validate on the Google Jigsaw toxic comments and Facebook hateful memes datasets to assess generalizability. Our results reveal that zero-shot LLM classification underperforms on these high-context tasks compared to supervised models. In contrast, integrating LLM-based semantic augmentation yields performance on par with approaches that rely on human-annotated data, at a fraction of the cost. These findings underscore the importance of strategically incorporating LLMs into machine learning (ML) pipeline for social media classification tasks, offering broad implications for combating harmful content online.
Disclaimer: This paper contains examples of explicit language that may be disturbing to some readers
Analyzing Offensive Content and Emotional Dynamics in Black Lives Matter Discourse on Twitter
No full textThe Black Lives Matter (BLM) movement seeks to spread
awareness and fight against social and racial injustice. In
2020, BLM-related discussions surged on social media after
the death of George Floyd and the protests that followed. Previous
works have qualitatively analyzed the scaling, dynamics,
and topics of BLM discussions on social media. However,
very few works have studied the offensive content, the
emotions expressed, and the topics of offensive discussions in
BLM-related discussions. In this measurement study, to examine
offensive language and emotion, we conduct a largescale
study of BLM discussions on Twitter. We first develop
a classifier that uses sentiment representation to aid offensive
language detection. We then develop an emotion classifier
based on deep attention fusion with sentiment features
to classify emotions. We further use topic modeling to analyze
the topics of offensive tweets. Our analysis of over 20
million tweets revealed that offensive tweets peeked in the
weeks following George Floyd’s death and rapidly decreased
but remained stable. The analysis further revealed that negative
emotions were the most expressed emotions. Offensive
reply network analysis reveals that most offensive replies are
unidirectional. Our contribution in this work is five-fold: (1)
We identify offensive content during BLM protests; (2) we
identify online emotions that were significant in the offensive
and non-offensive content during the protests; (3) we assess
the characteristics of users who replied offensively and
those who are the recipients of offensive content; (4) we assess
emotion dynamics across offenders and recipients; (5)
we identify the hot topics that most drove the offensive content
on Twitter. Our work offers important implications for
content moderation and the conscious and unconscious attitudes
towards the black/African American community
Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research
No full textBillions of individuals engage with Online Social Networks (OSN) daily. The owners of OSN try to meet the demands of their end-users while complying with business necessities. Such necessities may, however, lead to the adoption of restrictive data access policies that hinder research activities from "external"' scientists---who may, in turn, resort to other means (e.g., rely on static datasets) for their studies. Given the abundance of literature on OSN, we - as academics - should take a step back and reflect on what we have done so far, after having written thousands of papers on OSN.
This is the first paper that provides a holistic outlook to the entire body of research that focused on OSN - since the seminal work by Acquisti and Gross (2006). First, we search through over 1 million peer-reviewed publications, and derive 13,842 papers that focus on OSN: we organize the metadata of these works in the Minerva-OSN dataset, the first of its kind - which we publicly release. Next, by analyzing Minerva-OSN, we provide factual evidence elucidating trends and aspects that deserve to be brought to light - such as the predominant focus on Twitter or the difficulty in obtaining OSN data. Finally, as a constructive step to guide future research, we carry out an expert survey (n=50) with established scientists in this field, and coalesce suggestions to improve the status quo - such as an increased involvement of OSN owners. Our findings should inspire a reflection to "rescue" research on OSN. Doing so would improve the overall OSN ecosystem, benefiting both their owners and end-users - and, hence, our society
Coordinated Reply Attacks in Influence Operations: Characterization and Detection
No full textCoordinated reply attacks are a tactic observed in online influence operations and other coordinated campaigns to support or harass targeted individuals, or influence them or their followers.
Despite its potential to influence the public, past studies have yet to analyze or provide a methodology to detect this tactic.
In this study, we characterize coordinated reply attacks in the context of influence operations on Twitter.
Our analysis reveals that the primary targets of these attacks are influential people such as journalists, news media, state officials, and politicians.
We propose two supervised machine-learning models, one to classify tweets to determine whether they are targeted by a reply attack, and one to classify accounts that reply to a targeted tweet to determine whether they are part of a coordinated attack.
The classifiers achieve AUC scores of 0.88 and 0.97, respectively.
These results indicate that accounts involved in reply attacks can be detected, and the targeted accounts themselves can serve as sensors for influence operation detection
Integrated or Segregated? User Behavior Change After Cross-Party Interactions on Reddit
No full textIt has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community engagement after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if they increase their activity in communities of the opposing party, or in communities of their own party. We find that receiving a cross-party reply to a comment in a non-partisan discussion space is not significantly associated with increased out-party subreddit activity, unless the comment itself is already a reply to another comment. Meanwhile, receiving a cross-party reply is significantly associated with increased in-party subreddit activity, but the effect is comparable to that of receiving a same-party reply. Our results reveal a highly conditional depolarization effect following cross-party interactions in spurring out-party community engagement, which is likely part of a more general dynamic of feedback-boosted activity