Twitter Posts Research Articles

Performing Sentiment Analysis Using Natural Language Models for Urban Policymaking: An analysis of Twitter Data in Brussels

ObjectivesMobility is a core challenge to transition towards sustainability. Cities are, therefore, rethinking their mobility to reduce negative externalities such as (greenhouse) gas emissions or congestion. When trying to implement sustainable urban mobility plans, there is often resistance from citizens. This can indicate a disconnect between the public and policymakers due to a lack of participation, coupled with the fact that current data-collection methods often used (such as travel surveys) are limited in scope. Advances in big data analysis and user-generated content provide opportunities to gain deeper insights into citizens' perceptions of mobility policy changes. This paper explores how sentiment analysis through deep learning can be used in transport planning.Materials and MethodsIn this research, we analyse the sentiments of citizens towards recent changes in mobility policy in Brussels, Belgium, through Twitter data. We analyse 1998 tweets about changing mobility policy in Brussels between July 18th, 2019 (forming of the last Brussels regional government), and December 31st, 2022 (starting date of the analyses). For our analysis, we employ two pre-trained language models: XLM-T and GPT4.ResultsOur results show that the sentiment with regard to the new mobility interventions is, as reflected by Twitter posts, not overwhelmingly negative. Furthermore, we find that the performance scores of XLM-T when no domain-specific fine-tuning has occurred (zero-shot evaluation) is fairly low (0.48). Once the model is trained on our domain-specific data, it reaches an accuracy of 0.67. When using GPT4, the model reaches an accuracy of 0.66. Additionally, GPT4 seems better suited at identifying mismatched tweets, i.e. tweets using vocabulary that has a different sentiment than the one the tweets expresses (e.g., sarcasm). This might indicate that large language models might be better suited to obtain implicit sentiments expressed in a text.ConclusionsFrom a machine learning perspective, our experiments highlight the difficulty of recognising contextual sentiment (in this case, a sentiment towards changes in mobility policies), which may differ from the sentiment reflected in the vocabulary used. This is especially important if these two sentiments do not correspond, a problem both models struggled with. Additionally, we show that GPT4 can provide additional information when performing sentiment analysis by prompting it to attribute scores to texts. This paper opens new perspectives on understanding and addressing public sentiment in urban mobility policies. The advancements in language models, and the effective integration of user-generated content, can provide policymakers with a more comprehensive understanding of public sentiment, facilitating the implementation of certain policies.Clinical RelevanceNone.

Social Media and Real Estate: Do Twitter Users Predict REIT Performance?

This study investigates the impact of social media sentiment on indirect real estate market returns by utilizing a comprehensive natural language processing approach to identify relevant Twitter posts and extract sentiment from them. To handle the complex linguistic features inherent in social media messages, three different sentiment classifiers are compared. The findings suggest a significant relationship between monthly sentiment and REIT returns, which occurs in two phases: a short-term speculative reaction and a greater longer-term reaction related to actual changes in the real estate market. The study also highlights that while the conventional dictionary approach can identify this relationship, more sophisticated classifiers can achieve higher accuracy. Overall, the results demonstrate the valuable insights that can be gained from analyzing social media data and its potential impact on the real estate market.

English tweets on allergy: Content analysis and association with surveillance data.

Analysis of X (formerly Twitter) posts can inform on the interest/perceptions that social media users have on health subjects. In this study, we aimed to analyse tweets on allergic conditions, comparing them with surveillance data. We retrieved tweets from England on "allergy," "asthma," and "allergic rhinitis," published between 2016 and 2021. We estimated the correlation between the frequency of tweets on "asthma" and "allergic rhinitis" and English surveillance data on the incidence of asthma and allergic rhinitis medical visits. We performed sentiment analysis, computing a score informing on the emotional tone of assessed tweets. We applied a topic modelling approach to identify topics (clusters of words frequently occurring together) for tweets on each assessed condition. We analysed a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhinitis." Food-related words were preponderant on tweets on "allergy," while "eyes" was the most frequent meaningful word on "allergy rhinitis" tweets. We observed seasonal patterns for tweets on "allergic rhinitis," both in their frequency and sentiment - the incidence of allergic rhinitis medical visits was moderately to strongly correlated with the frequency (ρ = 0.866) and sentiment (ρ = -0.474) of tweets on "allergic rhinitis." For tweets on "asthma," no such patterns/correlations were observed. The average sentiment score was negative for all assessed conditions, ranging from -0.004 ("asthma") to -0.083 ("allergic rhinitis"). Tweets on "allergic rhinitis" displayed a seasonal pattern regarding their frequency and sentiment, which correlated with surveillance data. No such patterns were observed for "asthma."

#HandsUpDontShoot: Studying Coverage of Ferguson as a ‘Critical Incident’ from Journalism’s Recent Past

For seventy years, gatekeeping theory has described how news sources, news institutions, and audiences impact the way information is distributed and consumed through mass media. But when a Black teenager, Michael Brown, was fatally shot by police in Ferguson, Missouri, citizen accounts of him having his “hands up” quickly spread on social media, forcing journalists to acknowledge both the circumstances of the shooting and the role of social media in shaping the narrative. This historical study of the “recent past” considers how Brown’s shooting death served as a “critical incident” that forced journalists to reevaluate long-held practices in the face of citizen journalism on social media. Using textual analysis, this study examines local and national news coverage of the shooting and resulting protests, as well as subsequent investigations over the following weeks and months; it draws on content from the St. Louis Post-Dispatch, New York Times, and other outlets, as well as Twitter posts from citizens. The study, which places Ferguson in the historical context of breaking news coverage, finds social media narratives initially impacted the shape of news coverage, but over time journalists ultimately reverted to their traditional reliance on official sources.

Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting

This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .

Development of a Medical Social Media Ethics Scale and Assessment of #IRad, #CardioTwitter, and #MedTwitter Posts: Mixed Methods Study.

Social media posts by clinicians are not bound by the same rules as peer-reviewed publications, raising ethical concerns that have not been extensively characterized or quantified. We aim to develop a scale to assess ethical issues on medical social media (SoMe) and use it to determine the prevalence of these issues among posts with 3 different hashtags: #MedTwitter, #IRad, and #CardioTwitter. A scale was developed based on previous descriptions of professionalism and validated via semistructured cognitive interviewing with a sample of 11 clinicians and trainees, interrater agreement, and correlation of 100 posts. The final scale assessed social media posts in 6 domains. This was used to analyze 1500 Twitter posts, 500 each from the 3 hashtags. Analysis of posts was limited to original Twitter posts in English made by health care professionals in North America. The prevalence of potential issues was determined using descriptive statistics and compared across hashtags using the Fisher exact and χ2 tests with Yates correction. The final scale was considered reflective of potential ethical issues of SoMe by participants. There was good interrater agreement (Cohen κ=0.620, P<.01) and moderate to strong positive interrater correlation (=0.602, P<.001). The 6 scale domains showed minimal to no interrelation (Cronbach α=0.206). Ethical concerns across all hashtags had a prevalence of 1.5% or less except the conflict of interest concerns on #IRad, which had a prevalence of 3.6% (n=18). Compared to #MedTwitter, posts with specialty-specific hashtags had more patient privacy and conflict of interest concerns. The SoMe professionalism scale we developed reliably reflects potential ethical issues. Ethical issues on SoMe are rare but important and vary in prevalence across medical communities.

Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.

Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin-related Twitter posts (tweets). We analyzed original tweets with statin-related terms from 2010 to 2022 using a machine learning-derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin-related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative-sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans. Over the past decade, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin-related misinformation.

Tracking collective emotions in 16 countries during COVID-19: a novel methodology for identifying major emotional events using Twitter.

Using messages posted on Twitter, this study develops a new approach to estimating collective emotions (CEs) within countries. It applies time series methodology to develop and demonstrate a novel application of CEs to identify emotional events that are significant at the societal level. The study analyzes over 200 million words from over 10 million Twitter messages posted in 16 countries during the first 120 days of the COVID-19 pandemic. Daily levels of collective anxiety and positive emotions were estimated using Linguistic Inquiry and Word Count's (LIWC) psychologically validated lexicon. The time series estimates of the two collective emotions were analyzed for structural breaks, which mark a significant change in a series due to an external shock. External shocks to collective emotions come from events that are of shared emotional relevance, and this study develops a new approach to identifying them. In the COVID-19 Twitter posts used in the study, analysis of structural breaks showed that in all 16 countries, a reduction in collective anxiety and an increase in positive emotions followed the WHO's declaration of COVID-19 as a global pandemic. Announcements of economic support packages and social restrictions also had similar impacts in some countries. This indicates that the reduction of uncertainty around the evolving COVID-19 situation had a positive emotional impact on people in all the countries in the study. The study contributes to the field of CEs and applied research in collective psychological phenomena.

Leveraging distant supervision and deep learning for twitter sentiment and emotion classification

Nowadays, various applications across industries, healthcare, and security have begun adopting automatic sentiment analysis and emotion detection in short texts, such as posts from social media. Twitter stands out as one of the most popular online social media platforms due to its easy, unique, and advanced accessibility using the API. On the other hand, supervised learning is the most widely used paradigm for tasks involving sentiment polarity and fine-grained emotion detection in short and informal texts, such as Twitter posts. However, supervised learning models are data-hungry and heavily reliant on abundant labeled data, which remains a challenge. This study aims to address this challenge by creating a large-scale real-world dataset of 17.5 million tweets. A distant supervision approach relying on emojis available in tweets is applied to label tweets corresponding to Ekman’s six basic emotions. Additionally, we conducted a series of experiments using various conventional machine learning models and deep learning, including transformer-based models, on our dataset to establish baseline results. The experimental results and an extensive ablation analysis on the dataset showed that BiLSTM with FastText and an attention mechanism outperforms other models in both classification tasks, achieving an F1-score of 70.92% for sentiment classification and 54.85% for emotion detection.

Impact of social media on the evolution of English semantics through linguistic analysis

Social media (SM) influences social interaction in the age of digital media, impacting how languages develop. Since these networks play a role in daily life, they create new words and conceptual frameworks that define our contemporary society. The current investigation investigates Twitter, Facebook, and Reddit SM posts applying textual extraction. The seven-year temporal sample demonstrates significant semantic change caused by society and technology. The analysis notices the importance of new words, phrase meaning evolving, and sentiment changes in SM users’ English usage, proving their adaptability. The growing popularity of phrases like eavesdropping and doom-scrolling indicated how SM and daily life impact. This investigation distinguishes each platform’s unique linguistic features and digital developments by understanding language flow and leading research in the future.

A sentiment analysis approach for understanding users’ perception of metaverse marketplace

This research explores the user perceptions of the Metaverse Marketplace, analyzing a substantial dataset of over 860,000 Twitter posts through sentiment analysis and topic modeling techniques. The study aims to uncover the driving factors behind user engagement and sentiment in this novel digital trading space. Key findings highlight a predominantly positive user sentiment, with significant enthusiasm for the marketplace's revenue generation and entertainment potential, particularly within the gaming sector. Users express appreciation for the innovative opportunities the Metaverse Marketplace offers for artists, designers, and traders in handling and trading digital assets. This positive outlook is tempered by notable concerns regarding security and privacy within the Metaverse, pointing to a critical area for development and assurance. The study also reveals a substantial neutral sentiment, reflecting users’ cautious but interested stance, particularly regarding the marketplace's role in investment and passive income opportunities. This balanced view underscores the evolving nature of user perceptions in this emerging field. Theoretically, the research enriches the discourse on technology adoption, particularly in virtual environments, by highlighting perceived benefits and enjoyment as significant adoption drivers. These insights are invaluable for stakeholders in the Metaverse Marketplace, guiding the development of more secure, engaging, and user-friendly platforms. While providing a pioneering perspective on Metaverse user perceptions, the study acknowledges its limitation to Twitter data, suggesting the need for broader research methodologies for a more holistic understanding.

Navigating the Digital Neurolandscape: Analyzing the Social Perception of and Sentiments Regarding Neurological Disorders through Topic Modeling and Unsupervised Research Using Twitter

Neurological disorders represent the primary cause of disability and the secondary cause of mortality globally. The incidence and prevalence of the most notable neurological disorders are growing rapidly. Considering their social and public perception by using different platforms like Twitter can have a huge impact on the patients, relatives, caregivers and professionals involved in the multidisciplinary management of neurological disorders. In this study, we collected and analyzed all tweets posted in English or Spanish, between 2007 and 2023, referring to headache disorders, dementia, epilepsy, multiple sclerosis, spinal cord injury or Parkinson’s disease using a search engine that has access to 100% of the publicly available tweets. The aim of our work was to deepen our understanding of the public perception of neurological disorders by addressing three major objectives: (1) analyzing the number and temporal evolution of both English and Spanish tweets discussing the most notable neurological disorders (dementias, Parkinson’s disease, multiple sclerosis, spinal cord injury, epilepsy and headache disorders); (2) determining the main thematic content of the Twitter posts and the interest they generated temporally by using topic modeling; and (3) analyzing the sentiments associated with the different topics that were previously collected. Our results show that dementias were, by far, the most common neurological disorders whose treatment was discussed on Twitter, and that the most discussed topics in the tweets included the impact of neurological diseases on patients and relatives, claims to increase public awareness, social support and research, activities to ameliorate disease development and existent/potential treatments or approaches to neurological disorders, with a significant number of the tweets showing negative emotions like fear, anger and sadness, and some also demonstrating positive emotions like joy. Thus, our study shows that not only is Twitter an important and active platform implicated in the dissemination and normalization of neurological disorders, but also that the number of tweets discussing these different entities is quite inequitable, and that a greater intervention and more accurate dissemination of information by different figures and professionals on social media could help to convey a better understanding of the current state, and to project the future state, of neurological diseases for the general public.

Trans Youth Sport Bans and the Facilitation of Moral Panic: A Cross-Platform Comparison of 2022 Media Narratives

ABSTRACT This study incorporates moral panics as a lens for investigating discussions in US news reports and on social media surrounding anti-transgender youth sports legislation. A total of 210 newspaper articles and 6,405 Twitter posts from January 2022 through November 2022 were analyzed to highlight the proportion of content that incorporated moral panic elements of concern, hostility, consensus, and disproportionality within the discussion. While the frames advanced on the two platforms largely mirrored one another, social media posts were more likely to incorporate aspects of moral panic escalation (concern and hostility) than were news reports. Escalation elements stayed high on both platforms over the course of time. Pro-ban protests significantly rose on social media while equity/fairness arguments cross-state comparisons diminished as content moved closer to election day.

Misinformation and harmful language are interconnected, rather than distinct, challenges.

There is considerable concern about users posting misinformation and harmful language on social media. Substantial-yet largely distinct-bodies of research have studied these two kinds of problematic content. Here, we shed light on both research streams by examining the relationship between the sharing of misinformation and the use of harmful language. We do so by creating and analyzing a dataset of 8,687,758 posts from N = 6,832 Twitter (now called X) users, and a dataset of N = 14,617 true and false headlines from professional fact-checking websites. Our analyses reveal substantial positive associations between misinformation and harmful language. On average, Twitter posts containing links to lower-quality news outlets also contain more harmful language (β = 0.10); and false headlines contain more harmful language than true headlines (β = 0.19). Additionally, Twitter users who share links to lower-quality news sources also use more harmful language-even in non-news posts that are unrelated to (mis)information (β = 0.13). These consistent findings across different datasets and levels of analysis suggest that misinformation and harmful language are related in important ways, rather than being distinct phenomena. At the same, however, the strength of associations is not sufficiently high to make the presence of harmful language a useful diagnostic for information quality: most low-quality information does not contain harmful language, and a considerable fraction of high-quality information does contain harmful language. Overall, our results underscore important opportunities to integrate these largely disconnected strands of research and understand their psychological connections.

Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

Preserving a robust online community poses a significant difficulty due to the unrestricted flexibility members have in expressing themselves and behaving. This issue can be remedied through the implementation of user behavior monitoring and analysis, followed by the implementation of suitable actions. The objective of this research is to develop an intelligent dictionary using the genetic algorithm to identify and categorize Twitter posts related to criminal activities by collecting data from tweets. Once the data is preserved, graph analysis techniques are employed to evaluate interactions between users. Next, the user behavior is analyzed using metadata analysis, whereby the chronology associated with each user profile is obtained. Furthermore, the study analyzes the behavioral patterns of users over time. Afterward, a method based on rules is used to create a structure for aspect-based analysis of sentiment. This method assesses the subjectivity of the input text, distinguishing between factual information and personal opinions. Additionally, transformer-based sentiment analysis determines whether the tweet evokes positive or negative sentiment. Furthermore, the task involves constructing a model that can accurately categorize a tweet based on its relevance to criminal activity. Ultimately, the intelligent dictionary is utilized to identify and isolate anomalous behavior by selecting provocative profiles. A Twitter profile exhibiting significant similarity with criminal cases presents a perilous risk to society.

A social turn for Construction Grammar: double modals on British Twitter

Construction Grammar is an emerging theory of language, but the analysis of sociolinguistic variation is still relatively underdeveloped in the framework. In this article, we consider the representation of social meaning in Construction Grammar through a corpus-based analysis of double modals in British English on social media. We describe the use of double modals in a large corpus of geolocated Twitter posts, including presenting an inventory of observed double modals and maps showing the regional distribution of each of these forms. We find that double modals show a general northern pattern and are concentrated in the Scottish Borders. We also find various rare double modals that occur more widely across the UK. To account for these results, we propose a Construction Grammar account of double modals. We argue that defining double modals as grammatical constructions requires that aspects of their social meaning be delimited, especially register and region. Furthermore, we argue that double modals may be enregistered as dialect constructions, distinguished from standard constructions of British English. We conclude by considering the importance of incorporating social meaning into Construction Grammar, underlining the value of a Cognitive Sociolinguistic approach to grammatical theory.

Assessing the public discourse on Twitter: Reactions to the JUUL e-cigarettes ban in the United States.

JUUL is a high-nicotine pod-based vaping device that is popular among adolescents and young adults. On 23 June 2022, the US Food and Drug Administration (FDA) denied authorization to market JUUL, and ordered JUUL Labs to remove products from the US market. The next day, a US federal appeals court temporarily suspended the ban. The mixed public discourse surrounding the FDA ban warrants further investigation. This study examined Twitter data to describe public reaction to these announcements. Posts containing terms 'JUUL' and/or '#JUUL' (N=97548 unique tweets) were collected from 23 June to 3 July 2022, from Twitter's Streaming Application Programming Interface (API). After removing retweets, we used an inductive approach to become familiar with the data, generated a codebook, and conducted a content analysis on a random sample of n=4000 tweets. A total of 2755 (68.9%) tweets discussed JUUL in the context of the FDA ban. News (n=1425/2755; 51.7%) about the JUUL ban, government distrust (n=588; 21.3%), and individual rights (n=253; 9.2%) were the most prevalent themes. Less commonly discussed themes included inconsistencies between policies (n=174; 6.3%), mentions of switching to other products (n=162; 5.9%), smoking cessation (n=99; 3.6%), and craving for JUUL (n=94; 3.4%). Sentiment analysis of JUUL ban-related posts (n=2755) demonstrated that 1989 (72.2%) tweets were categorized as neutral, while anti-ban posts (n=566; 20.5%) were more prevalent than pro-ban posts (n=200; 7.3%). Besides straightforward announcements of the JUUL ban and its suspension, Twitter posts discussed government distrust, individual rights, and policy inconsistencies. While most posts conveyed neutral sentiments, anti-ban posts were almost three times more prevalent than pro-ban posts. Our findings suggest that text-based social media platforms like Twitter may be an effective instrument to understand opinions, attitudes, and beliefs regarding the FDA's JUUL ban.

News Framing and Platform Affordances in Social Media

ABSTRACT Framing refers to the construction of narratives with specific intentions, emphasizing certain aspects of the story and omitting or obscuring others, thus affecting how audiences perceive social reality. Since the 1960s, journalism researchers have been interested in this phenomenon, developing multiple theories. This research seeks to analyze how framing connects with the affordances of platforms in international conflict news narration on official Instagram and Twitter profiles of Spanish and Qatari media. By examining these platforms, we can delve deeper into the use of framing adapted to the digital environment and explore nuances and implications. To this end, we meticulously analyzed 251 Instagram and Twitter posts from three leading newspapers in both countries, applying grounded theory through an abductive content analysis. This comprehensive approach allowed us to define codes that help us understand how the affordances of the platforms impact the construction of meaning and the establishment of new frames. In conclusion, this study sheds light on how this digital scenario contributes to the deployment of digital frames derived from the unique affordances provided by these platforms, ultimately shaping our perception, and understanding of news narratives and offering insights into the evolving nature of media communications.

Promoting public health equity through strategic information campaigns on social media during the COVID‐19 pandemic: An analysis of pandemic health information campaigns on Twitter in Houston, Texas

AbstractThroughout the coronavirus disease 2019 (COVID‐19) pandemic, reports have indicated vulnerable communities have suffered disproportionately from chronic illness, higher death rates, and unequal access to healthcare. This calls into question the equity of the nationwide vaccination program launched in the United States in 2021, a program with the potential to mitigate the disproportionate burden of the pandemic in these communities. This study investigates the extent to which local government agencies, elected officials, and community‐based organizations (CBOs) engaged in equitable information campaigns during the COVID‐19 pandemic to address health barriers common in vulnerable communities, including issues of access, fear, and language barriers. Using a sui generis data set of COVID‐19 testing and vaccine‐related Twitter posts from local government agencies, elected officials, and CBOs from Houston, Texas, we find that local government agencies were more likely than elected officials and CBOs to engage in equitable information campaigns and that among elected officials, Democrats were more likely than Republicans to engage in equitable messaging. These findings have important implications for communication strategies such as targeted and culturally sensitive messaging. These findings also signal an opportunity for elected officials and CBOs to engage further with their constituents and social networks to share public health information on their public communication platforms.

The Internet is a scary place: How does evidence source and examinee race or ethnicity influence determinations of threat?

Violent rhetoric online is becoming increasingly relevant to the practice of forensic mental health assessment as examinee's virtual lives may transform into real-world acts of violence. With the rise of a diverse subculture of violent online communities, the aim of the present study was to inform how concerns with online sources of collateral data and racial/ethnic biases may influence determinations of violence potential. Using an experimental design, jury-eligible participants (N=278) and forensic mental health experts (N=78) were presented with mock Twitter (now referred to as X) posts that varied by data source (i.e., how information was accessed) and the examinee's race/ethnicity. Results showed no differences in participants' ratings of data credibility, how much weight they would place on the posts in a threat assessment, or how likely the examinee was to act violently against his intended target. Implications regarding the interpretation of social media evidence, relevant limitations, and future research are discussed.