In today's digital landscape, social media platforms have become important areas for disseminating information, ranging from legitimate discourse to misinformation, especially on critical topics such as Climate Change. This study employs claim detection and clustering techniques to analyze misleading information within an initial dataset of videos. Initially, the study identified 5,352 videos out of a total of 8,151 that warranted further investigation. Utilizing clustering methods, it was discovered that the prevalence of misinformation was surprisingly lower than anticipated. Most of the clusters showcases content that promotes sustainability and raises environmental awareness, strengthened by corroborated information of fact-checking agency EFE Verifica. Conversely, there are two clusters that focuses on videos propagating misinformation, conspiracy theories, active discussion and debate, highlighting the necessity of consuming media with caution. Looking ahead, combating misinformation necessitates the enhancement of digital literacy and the cultivation of critical thinking skills. This research aims to leverage technology and verified information from credible organizations to identify, analyze, and mitigate the influence of misleading content on social media, thus better understanding its dynamics and reducing its adverse impacts.
The spread of climate change misinformation on TikTok has become a serious concern for both environmental scientists and social media analysts. Understanding the dynamics and impact of this phenomenon requires in-depth analysis of the most viral content on the platform Viral content on TikTok often has several key characteristics: visually appealing, emotional and easy to understand. In the context of climate change misinformation, these elements are often used to attract audiences and spread false narratives. For example, many viral videos contain dramatic images of natural disasters or melting ice caps combined with misleading or inaccurate explanations. These videos often contain sensational and apocalyptic predictions, which can evoke strong emotional responses and encourage users to share the content widely.
A perfect example is videos falsely claiming that climate change is a fraud committed by governments or corporations for financial gain. These clips often feature conspiracy theory imagery, which is particularly appealing to viewers who are already skeptical of mainstream science. Additionally, such content creators often pretend to be tipsters, exploiting the feeling of inside information to increase their authority and attract more views.
TikTok’s viral misinformation patterns are influenced by the platform’s algorithm, which prioritizes content that generates high levels of engagement. Videos with lots of likes, comments and shares appear more frequently on the For You page, which exponentially increases their reach. This creates a feedback loop in which misinformation continuously reaches new users, thereby increasing its spread.
Collaboration features also play a key role in the spread of misinformation. These features allow users to directly interact with existing videos by adding their own comments or creating reaction videos. Not only does this increase the visibility of the original content, but it also creates a sense of community and brainstorming around misinformation, which further contributes to its virality.
The widespread dissemination of climate change misinformation on TikTok has significant
implications for public perception. Studies such as [
In this research, we undertake an analysis of misinformation around climate change in TikTok. We ifltered 5,352 from a total of 8,151 videos related to conversations around climate. Then, we extracted information from the videos, such as keywords and the transcription. Then we applied clustering algorithms to identify diferent subtopics and conversations, which allowed us to identify groups promoting negationist theories about climate change.
The rest of this article is organised as follows. Section II presents an analysis of the state-of-the-art literature, Section III describes the methodology, Section IV the results, Section V analyses the presence of hoaxes and finally, Section VI presents a series of conclusions.
In the digital age, social media platforms have become a prime battleground for the dissemination of
information and the spread of ideas, both legitimate and otherwise. One such arena is the discussion
surrounding climate change, where the impact of social media, AI and algorithmic systems has become a
growing concern [
Several studies as [
Many studies have investigated the spread of misinformation on social media. [
[
Also, the purpose of the study [
This section describes the methodology employed to extract, filter and analyze all extracted information from the social network used in this research: TikTok.
The initial phase of our research involved the systematic collection of data using TikTok’s research API
designed for developers [
We meticulously extracted metadata from videos associated with a selection of environmentally relevant hashtags. These hashtags included #climatechange, #ecofriendly, #sustainability and #ecotok.
Upon securing the initial dataset, we conducted a comprehensive analysis to identify the most frequently used hashtags within these videos. This analysis led to the discovery of additional pertinent hashtags such as #zerowaste, #naturetok, #globalwarming, #climatecrisis, #savetheplanet, #ecology, #plasticfree, #sustainable, #savetheworld, #recycle, #recycling, #upcycling, #saveourplanet, #upcycle, #bethechange, #environment, #climateaction, #climateemergency, #climate, #plasticpollution, #plasticwaste, #savetheocean, #saveouroceans and #eco. This iterative process was repeated meticulously until we had compiled an exhaustive list of the most prevalent hashtags in our dataset, which are those shown above.
Subsequent to the environmental hashtag analysis, we turned our focus to the investigation of misinformation related to climate change. We identified and collected metadata associated with hashtags that propagate misinformation, such as #climatelies, #climatehoaxx, #climatehoaks, #climatelie, #climatehoax, #globalwarmingisfake, #globalwarminghoax, #globalwarmingisahoax, #carbonkleptomania, #globalcooling, #climatechangehoax, #noclimateemergency, #climatescam, #weathermanipulation, #stopglobalwarming and #globalwarmingsucks. And just like before, we repeated this iterative process until we had compiled an exhaustive list of the most prevalent hashtags that propagate misinformation in our dataset, which are those shown above.
Through this rigorous process, we accumulated a total of 8,151 video metadata entries, covering the period from January 2020 to June 2024.
Once all videos were retrieved, the second step was to extract the audio channel, converting from .mp4
to format .mp3, and then we used the Whisper model [
The next step was to analyze the transcriptions. First, we divided the transcriptions into sentences
and two pre-trained models were loaded: the SentenceTransformer model [
Our aim was to do clustering to all transcriptions and to obtain the most relevant keywords of each cluster. For this, to find the optimal number of clusters for K-Means, the elbow method and silhouette scores were used, and it was found that this number was ten clusters.
Then, we applied K-Means clustering to the embeddings and when we analyzed the most relevant keywords of each cluster we obtained some keywords that didn’t make sense on the issue of climate change, like “foryourpage”, “fyp” or “for watching”, so we established them as stopwords but the results obtained were not much better. After trying several things we realized that many of the videos only had songs or phrases that were not very relevant as sound, so we decided to leave the transcriptions aside.
The second option was to use the “video_description” that we obtained when we extracted metadata
from videos using TikTok’s research API designed for developers [
As we mentioned before, we accumulated a total of 8,151 video metadata entries, covering the period from January 2020 to June 2024, so the data collection we used in the end was larger than expected. To find the optimal number of clusters for K-Means, the elbow method and silhouette scores were used, and it was found that this number was seven clusters. Then, we applied K-Means clustering to the descriptions of the videos and we obtained seven clusters well-defined, which are shown in the section 4.
The image below, Fig. 1, represents the clustering of descriptions from 8,151 TikTok videos. We employed K-means clustering and Principal Component Analysis (PCA) to visualize and understand the thematic grouping of these transcriptions. The scatter plot shows the distribution of seven clusters, diferentiated by colors. This section provides a detailed analysis of these clusters, discussing the most relevant keywords, potential misinformation and aggregate statistics for likes, views and comments.
The clustering algorithm identified seven distinct clusters among the videos descriptions. Each cluster represents a group of videos with similar content based on their descriptions. The cluster analysis is presented below: • Group 0. This cluster, represented in the scatter plot by the color purple, contains 1,260 videos.
This cluster is characterized by prominent keywords like "sewing", "upcycling", "thrift", "thriftflip" and "fashion". This suggests a focus on sewing techniques, clothing upcycling and sustainable fashion practices. • Group 1. It is identified in the scatter plot by the color dark blue, comprises 1,300 videos. This cluster is characterized by content related to TikTok, duets, life hacks and fashion. Common keywords include "duet with", "duet", "lifehacks" and "tiktok". This indicates a focus on social interaction and entertainment. • Group 2. It is represented in the scatter plot by the color blue, contains 1,665 videos. This cluster is distinguished by its focus on sustainability, environment and ecological awareness. Keywords include "sustainability", "environment", "eco", "nature" and "ecofriendly". This reflects a strong interest in sustainable practices and environmental protection. • Group 3. Identified in the scatter plot by the color turquoise, comprises 1,243 videos. This cluster is characterized by discussions on weather manipulation, chemtrails and climate-related conspiracy theories. Keywords include "weathermanipulation", "chemtrails", "geoengineering", "weathermodification" and "conspiracytiktok". • Group 4. This cluster, represented in the scatter plot by the color green, contains 1,139 videos.
This cluster focuses on climate change, global warming, climate action and its controversies. Common keywords include “climatechange”, “globalwarming”, “climatecrisis”, “climateaction” and “gretathunberg”. Also included are terms that deny or question climate change (“climatelies”, “globalwarmingisfake” and “climatehoaxx”). The activist “gretathunberg” is mentioned, suggesting discussions about her influence. • Group 5. This cluster, identified in the scatter plot by the color light green, comprises 636 videos.
This cluster is characterized by its focus on climate action, saving the planet and environmental awareness. Keywords include “stopglobalwarming”, “savetheworld”, “savetheplanet” and “saveouroceans”. • Group 6. This last cluster, represented in the scatter plot by the color yellow, contains 908 videos.
This cluster is characterized by content related to recycling, waste reduction and sustainable practices. Keywords include “recycle“, “recycling”, “zero waste”, “reuse” and “crafts”.
To provide the big picture of the results, Table 1 lists the most frequent keywords for each cluster, The total number of videos in each cluster and the total likes, views and comments for videos in each cluster. This data highlights the distribution of videos across the seven thematic clusters and provides insight into the engagement levels of videos in diferent thematic groups.
An analysis of the keywords and content themes across clusters reveals distinct focal areas and potential areas of misinformation. Clusters like Cluster 0 and Cluster 6 emphasize practical advice and advocacy for environmental issues, showcasing keywords related to sewing, upcycling, recycling and sustainable living. For instance, Cluster 0 includes terms like “sewing”, “upcycling” and “thrift” while Cluster 6 is characterized by “recycle”, “recycling” and “plasticfree”. These clusters tend to present more straightforward and educational content, making them less likely to contain misinformation.
In contrast, Cluster 3 stands out due to the prevalence of terms such as "weathermanipulation", "chemtrails" and "geoengineering", pointing to content related to conspiracy theories. This cluster is more likely to mix factual information with misleading claims, which can negatively influence public perception.
Also Cluster 4, despite containing terms as "climatechange", "globalwarming", "climatecrisis" and "climateaction", it also contains terms as "climatelies", "globalwarmingisfake" and "climatehoaxx; which means it focuses on climate change, global warming, climate action and its controversies.
The focus on conspiracy theories contrasts with the more practical and educational nature of the content in Clusters 0 and 6.
When considering the total likes, views and comments of these clusters, significant diferences emerge. Cluster 5, for instance, which includes keywords like "stopglobalwarming" and "savetheworld" has the highest number of likes (3,053,581). This suggests that videos in this cluster receive higher acceptance and engagement from users, possibly due to the urgency and global appeal of climate change messaging.
Despite having fewer likes, Cluster 6 has the highest number of views (31,976,184). This indicates that videos in this cluster may be viewed more frequently, possibly due to a growing interest in recycling and
Keywords and Frequency sewing (37) upcycling (24) thrift (24) thriftflip (20) fashion (19) duet with (59) duet (47) bethechange (28) tiktok (25) lifehacks inspiration (19) sustainability (63) environment (40) sustainable (40) ecology (37) eco (27) weathermanipulation (338) chemtrails (136) weathermodification (69) weathermanipulation lexky (60) coveringthesun weathermanipulation (51) climatechange (238) globalwarming (116) global warming (67) climate change (47) climatecrisis (36) stopglobalwarming (162) savetheworld (87) savetheplanet (32) globalwarming (29) saveouroceans (18) recycle (126) recycling (118) recycled (27) plasticfree (18) waste (15) 1,260 1,160,419 11,666,224
15,321 1,300 839,619 6,303,510
40,536 1,665 1,778,118 18,963,898
39,140 1,243 681,355 3,707,237
57,980 1,139 1,511,484 10,797,769
48,182 636 908 3,053,581 17,783,375
52,676 1,621,028 31,976,184 17,701 zero-waste topics. The higher view count suggests that the audience for recycling content is substantial, even if individual engagement through likes is lower.
Cluster 1, by the other hand, characterized by keywords such as "duet with", "duet" and "bethechange" highlights a significant trend in social media engagement. With 1,300 videos, Cluster 1 focuses on collaborative content, challenges and inspirational messages.
Cluster 3, which includes conspiracy-related keywords, has the highest number of comments (57,980), and Cluster 4 which also mentions "gretathunberg", has the third higher number of comments (48,182), indicating active discussion and debate. This could be due to the controversial nature of the topics, which often elicit strong opinions and engagement from viewers, as we saw in 1. In comparison, Cluster 2, which focuses on sustainability and ecology, has a lower number of comments (39,140) but still shows significant engagement, reflecting interest in environmental issues.
In the digital age, the spread of misinformation, which is often mistakenly believed to be reliable, is a serious problem. As claim detection technology advances, the models for the detection of potentially misleading content have become more accurate. We used an automated claim detection model from Huggingface1 flagged a significant number of videos, suggesting they were worthy of fact-checking. Specifically, 5,352 videos were initially identified as requiring review due to concerns of possible misrepresentation.
However, after further analysis using clustering techniques, we discovered an interesting finding: the actual number of videos related to disinformation and fake news was significantly lower than the original estimate, as we saw in 4.1. This diference highlights the importance of using more diverse methods to distinguish reviewable content from non-reviewable content.
Using a clustering algorithm allows us to divide tagged videos into distinct groups based on their subject content and inferred features. It’s worth noting that we’ve divided these videos into seven groups, each revealing unique interaction patterns and thematic focus.
Group 0 emerged as a hub for sutainable fashion practices, clothing upcycling and sewing techniques.
Our validation process, including consultation of correborated information of fact-checking agency EFE
Verifica [
Group 2 is distinguished by its focus con sustainability, environment and ecological awareness. It was underscored a commitment to promoting responsible sustainability and ecological discourse. Group 3 is characterized by discussions on weather manipulation, chemtrails and climate-related conspiracy theories. Despite not all videos in this cluster perpetuating misinformation, those that did sparked intense debates and polarized discussions among viewers. Group 4 focuses on climate change, global warming, climate actions and its controversies. Although many of the videos are based on climate actions, there are also some other certain controversies and debates.
Group 5 is characterized by its focus on climate action, saving the planet and environmental awareness. Our validation process, confirmed the absence, mostly, of misinformation associated with these keywords. Group 6 is characterized by content related to recycling, waste reduction and sustainable practices. The commitment to promoting responsible sustainability and recycling practices was highlighted.
As we have seen, in the digital era, social media platforms have become critical battlegrounds where misinformation about critical issues like climate change proliferates. Our study utilized claim detection and clustering techniques to analyze a substantial dataset of videos. Initially flagging 5,352 videos for potential misinformation, we found through clustering that the actual prevalence of misinformation, particularly in Cluster 3 and some in Cluster 4, despite existing, was lower than expected.
The rest of clusters predominantly featured content promoting sustainability and environmental
awareness, corroborated by information of fact-checking agency EFE Verifica [
In summary, our study contributes to understanding and addressing the challenges posed by misinformation in digital media, emphasizing the importance of informed media consumption and collaborative eforts in safeguarding information integrity. 1https://huggingface.co/Nithiwat/xlm-roberta-base_claim-detection
This work has been funded by Grant PLEC2021-007681 (XAI-DisInfodemics), by the “European Union” or by the “European Union NextGenerationEU/PRTR”, by grant PCI2022-134990-2 (MARTINI) of the CHISTERA IV Cofund 2021 program, funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”, by Calouste Gulbenkian Foundation, under the project MuseAI Detecting and matching suspicious claims with AI and by European Comission under IBERIFIER Plus Iberian Digital Media Observatory (DIGITAL-2023-DEPLOY- 04-EDMO-HUBS 101158511).