HOTVCOM: Generating Buzzworthy Comments for Videos (2024)

Abstract: In the era of social media video platforms, popular “hot-comments” play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or “danmaku” in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces HOTVCOM, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the ComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets.

text: Findings of the Association for Computational Linguistics ACL 2024, pages 2198–2224 August 11-16, 2024 ©2024 Association for Computational Linguistics
duces HOTVCOM, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the ComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets.
1 Introduction With the increasing prevalence of video content on digital platforms, there is an evident significance of video comments in amplifying video reach. Specifically, “hot-comments” have the potential to attract considerable user interaction, substantially increasing a video’s user impressions, which is essential for product marketing and branding. A typical hotcomment often meets specific standards: receiving a larger number of likes and replies, being highly pertinent to the video content, and including the elements that resonate with viewers, as illustrated in Fig. 1.
However, the prevailing literature, such as the work by Ma et al. (2019) and Wang et al. (2020), largely concentrates on generating descriptive com-
∗ Work done during an internship at Ant Group. † Qingpei Guo is the corresponding author.
ments or “danmaku”, which are closely tied to certain video moments, offering instantaneous reactions. While these comments do engage viewers to some extent, their potential to highlight the entire short-video content or foster deep user interactions is somewhat limited. When aiming to elevate a video’s visibility, these real-time reactions might not be as influential as those hot-comments. Additionally, most research, including that by Sun et al. (2023b), predominantly focuses on English comments, leaving a gap in the domain of Chinese hot-comment generation.
In this paper, we construct the first Chinese video hot-comment generation dataset HOTVCOM, including video titles, descriptions, captions, audio speeches, keyframes, and engagement records. Compared to the English dataset from Sun et al. (2023b), our dataset HOTVCOM is more comprehensive, encompassing 93k videos with 137 million comments, making it the largest of its kind. In addition, for the convenience of analysis and targeted generation of comments, videos in
HOTVCOM are further categorized into different themes. Such a categorization helps language models to understand the context, therefore generating hot-comments that resonate with specific
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themes accordingly. A key challenge in video hot-comment generation lies on assessing whether the generated comments are truly impressive to users to boost the interaction. The existing work primarily utilizes ROUGE (Ma et al., 2019; Wang et al., 2020) or the number of likes (Sun et al., 2023b) as metrics. However, these metrics might not always reflect the genuine engagement of a comment. In this paper, we propose a novel comprehensive evaluation metric including the informativeness, relevance, creativity besides user engagement (i.e. likes and replies) of a comment, which thus reveals a well-rounded understanding of a comment’s ‘hotness’. With the guidance of our evaluation metrics, we then propose a novel video hot-comment generation framework named ComHeat. We implement the Supervised Fine-Tuning technique to generate preliminary comments and then enhance them with reinforcement learning. By incorporating knowledge-enhanced Tree-of-Thought method, the comments are further refined to improve the chance of their popularity.
In summary, our contributions are:
• We work on a novel task namely video hotcomment generation. To achieve this, we construct the largest Chinese video comment dataset including 93k videos with 137 million comments, named HOTVCOM.
• We introduce a novel comprehensive evaluation metric for video hot-comments generation, including informativeness, relevance, creativity, and user engagement, bridging the gap in qualitative comment analysis.
• We propose the ComHeat framework, which incorporates visual, auditory, and textual aspects, for generating engaging comments for Chinese short videos using reinforcement learning and Tree-of-Thought.
• Empirical results showcase that our ComHeat framework outperforms existing baselines on the newly-constructed dataset and also excels on other video comment generation and captioning tasks.
2 Datasets In this section, we construct a large-scale Chinese short video comment dataset named HOTVCOM including 94k short videos from Douyin with 137
million comments, where the entire process is shown in Fig. 2. We also conduct an extensive exploratory data analysis as shown in Fig. 5.
We initially collect Douyin videos from various themes in reverse chronological order up till 100k. Next, we conduct Optical Character Recognition (OCR) with PaddleOCR library 1 and Automatic Speech Recognition (ASR) with Xunfei open platform 2 on the videos to obtain their video captions and audio speech, respectively, and capture key frames of videos with the K-means clustering algorithm. After that, we also adopt PaddleOCR to extract the video’s title, creation time, publishers’ profile and engagement information, which includes the number of likes, comments, shares, and favorites, as well as each comment with its content, commenter’s profile and engagement information, which includes the number of likes and replies. Furthermore, from the tags marked with “#” in titles and themes provided by the Douyin platform, we categorize the videos with GPT-4 3 into 20 themes, including pets, food, etc. We also provide the descriptions for the video content and keyframes, encompassing scenes, objects, primary actions, atmosphere, and emotions with the help of video-ChatGPT (Li et al., 2023) and miniGPT4 (Zhu et al., 2023), respectively. More details are shown in Appendix A.
To maintain comment quality, we filter out comments with emojis, ASCII characters below 127, and those with less than 1 character or more than 50 characters. We also remove the comments with profanity, political content, negative tones, or promotional intent. The short videos with 0 or 1 comment are also discarded to avoid long-tail bias. In the end, we have 94k videos with 137 million comments, including video captions, audio texts, keyframes, engagement information, etc. The statistics of
HOTVCOM are shown in Table 1. On average, these videos are around 96.44 seconds long, indicating a preference for 1-2 minute content. The titles and descriptions average 43.62 and 419.22 characters, offering viewers a good context. Engagement information, such as the average number of likes of 25230.45 but a median of 3986, highlight the variability in video popularity. Similarly, while the mean number of comments is 1446.71, a median of 195 comments suggests the user en-
1https://github.com/PaddlePaddle/PaddleOCR 2https://raasr.xfyun.cn/v2/api 3https://chat.openai.com/
hot-comments dataset HOTVCOM.
gagement is concentrated on specific viral content. This dataset showcases the diverse content and engagement levels on the most popular short video platform in China. The more exploratory analysis on the constructed HOTVCOM is illustrated in Fig. 5 in Appendix B.
3 Evaluation We develop comprehensive evaluation metrics for hot-comments from four main aspects: informativeness, relevance, creativity, and user engagement.
The informativeness score I quantifies the utility of the information conveyed by a comment from lengths penalty and vocabulary diversity. Lengths penalty, denoted as Lp, quantify the lengths appropriateness of a comment. Vocabulary diversity, denoted as Vd, is calculated as the ratio of total bigrams to unique ones of this comment. The calculation process of informativeness score is as follows:
Lp =    L Lmin if L < Lmin L Lmax if Lmin ≤ L ≤ Lmax
1− α× (L− Lmax) if L > Lmax,
Vd = Tn Un , I = wI1 × Lp + wI2 × Vd,
(1)
where L denotes the actual length of a given comment, Lmin and Lmax specify the optimal length
boundaries of the comment length, which are set at 1 and 50, respectively, meaning that the comment length ranges from a minimum of 1 to a maximum of 50 characters. The constant α, between 0 and 1, adjusts penalties for a comment that go beyond the optimal length. Tn and Un signifying total and unique bigrams of a comment, respectively. wI1 and wI2 are trainable weights.
The relevance score R quantifies the alignment of a comment to the video content through two primary dimensions: keyword and context matching degree. The keyword matching degree, denoted by Dk, is the proportion of words in a comment resonating with the keywords of video captions that are extracted with ChatGPT. The context matching degree, denoted by Dc, is derived from cosine similarity between video captions and a corresponding comment. The calculation process of relevance score is as follows:
Dk = Nx Nk , Dc = Com ·Vid ||Com||2 × ||Vid||2 ,
R = wR1 ×Dk + wR2 ×Dc (2)
where Nx denotes the number of words or phrases in a comment that matches the keywords in video captions, and Nk is the total number of keywords extracted from the video captions with ChatGPT. Com and Vid are the representation of a comment and the corresponding video, respectively. wR1 and wR2 are trainable weights. For the robustness of the evaluation by ChatGPT, we repeat the process five times to obtain the most common result. We find that the consistency rate across the five iterations reaches over 80%. Additionally, we randomly sample 1000 cases and confirm that the results indeed align with our definition of keywords.
The creativity score C offers a quantitative assessment of a comment’s distinctiveness and novelty, which encompasses two primary metrics: the
rhetorical technique score, denoted as Sr, such as metaphors and irony, and the trending term score, denoted as St. The calculation process of the creativity score as follows:
Sr = 1
1 + e−kr(xr−br) , St =
1
1 + e−kt(xt−bt) ,
C = wC1 × Sr + wC2 × St (3)
where xr and xt represent the occurrence of rhetorical techniques and trending terms in a comment, respectively, which are both counted by ChatGPT. wC1 and w C 2 are also trainable weights. Specifically, the sigmoid function is employed to modulate Sr and St within the [0,1] interval. Experimentally, the optimal values for kr and kt are 1, as well as br and bt are -1, to ensure a score close approximate 0.1 when the count is zero, therefore achieving optimal score differentiation.
The user engagement score U represents the level of users’ interaction of a comment, which mainly includes likes and replies. The calculation process of user engagement score is as follows:
U ′ = wU1 ×Nl + wU2 ×Nr, U = 1
1 + e−ku(U′−bu) (4)
where Nl and Nr represent the number of likes and replies in a comment, respectively. wU1 and w U 2 are also trainable weights. Similarly, we also adopt the sigmoid function to modulate U ′ within the [0,1] interval.
Finally, the comprehensive score F of a comment is defined as:
F = wI × I + wR ×R+ wC × C + wU × U (5)
where wI , wR, wC , and wU are trainable weights assigned to the informativeness score, relevance score, creativity score, and user engagement score, respectively.
Furthermore, we manually score each metric (informativeness, relevance, creativity, and user engagement) with a binary 0 or 1. We set the threshold at 0.5 for each metric and use the AUC (Area Under Curve) to calculate the consistency between manual scoring and automatic scoring for each metric. We find that AUC of informativeness equals 0.83, AUC of relevance equals 0.86, AUC of creativeness equals 0.87, and AUC of user engagement equals 0.80. It suggests that the alignment of each metric with human judgment.
4 Methods We propose a Chinese video hot-comments generation framework named ComHeat as shown in Fig. 3.
Initial comments are first generated by the LLMs through supervised fine-tuning. Then we train a reward model based on the comprehensive score of comments and adopt reinforcement learning to refine the popularity of the generated comments. Finally, we utilize knowledge-enhanced Tree-ofThought method for further optimization to generate hotter comments.
4.1 Visual Feature Extraction The aim of this step is to bridge the semantic gap between videos and comments. We first leverage embeddings from key video frames inspired by the Flamingo approach (Alayrac et al., 2022). Next, recognizing the significance of event progression in videos, we preserve the keyframe sequence via positional embeddings. This ensures the sequence’s essence and progression are encapsulated for subsequent processing. Mathematically:
Fk = LVM(k), S = ∑
k∈K Fk ⊕ Pk (6)
Where Fk represents features from the k-th keyframe, extracted by LVM, Pk represents the positional embedding for the k-th keyframe, S represents the serialized sequence of keyframe embeddings, and K represents a video’s keyframe set.
4.2 Supervised Fine-Tuning We leverage an LLM, such as baichuan213B (Yang et al., 2023a), for Supervised FineTuning (SFT) with the aim of generating hotcomments that resonate with the ground truths. The input encompasses both textual information (titles, video captions, audio speeches, video descriptions) and visual features extracted in the last step. Firstly, textual information, denoted by T , is encoded to a dense vector Te. Concurrently, the visual features, represented by S, are transformed via a fully connected layer, ensuring compatibility with text dimensions. These modalities are then weighted fused linearly as follows:
Te = Enc(T ), St = FC(S), Em = α · Te + β · St (7) where α and β are adjustable weights. This integrated feature, Em, is decoded to generate hotcomments of videos. Specifically, during the training phase, the loss function, denoted as LSFT, integrates two components, including the inherent cross entropy of the SFT process, denoted as LCE, and the Mean Squared Error (MSE) that quantifies the difference between the comprehensive scores
of a predicted hot-comment and the ground truth, denoted as LF, as follows:
LSFT = w S 1 · LCE + wS2 · LF, (8)
where wS1 and w S 2 are trainable weights.
4.3 Reinforcement Learning The primary goal of this step is to align the generated comments with human preferences based on Reinforcement Learning (RL) using a reward model. The reward model associates the predicted hot-comment xi with the ground truth comment yi to compute a reward r = R(xi, yi).
Initially, we utilize comprehensive scores to assess 1% of the comments generated in the SFT process. This yields a a ranked sequence for an LLM, represented as {c1, c2, . . . , cn−1, cm}. Subsequently, using this sequence, we train a reward model. We adopt a powerful LLM, named baichuan2, and replace its softmax layer with a linear one. The reward model takes a comment as the input and returns a score that indicates the comment’s quality. For training, we collate responses from the ranking sequence and apply the Pairwise Ranking Loss, depicted as follows:
Lr = − 1(k 2 )E(x,yw,yl)∼D[log(σ(rθ(x, yw)− rθ(x, yl)))],
(9)
where x denotes the original comment, yw and yl represent the comments with higher and lower scores in the given ranking pair, respectively. rθ is the scalar output from the reward model, D is the set of ranking pairs, and K is the number of comments generated during SFT. Through this process, the reward model assigns higher scores (rewards) to high-quality comments and lower scores (penalties)
to inferior comments, effectively imitating human’s preferences.
Specifically, rewards comprise basic rewards, determined by user engagement scores, and auxiliary rewards determined by informativeness, relevance, and creativity scores. The basic rewards are sourced from evaluations by human annotators. Correlation coefficients are then computed between user engagement and one of the scores in informativeness, relevance, and creativity for each video. After that, the reward model adopts the basic and auxiliary rewards as the final rewards of a comment as follows:
wi = corr(xi, ui)∑n
j=1 corr(xj , uj) , r = BR +
n∑
i=1
wi · ARi (10)
where wi represents the weight for one of the scores in informativeness, relevance, and creativity for a comment. BR and AR represent the basic rewards and the auxiliary rewards, respectively.
After that, we feed comment x generated by the SFT model into the RL model πRLϕ to obtain a more human-preferred comment y. We first input (x, y) into the reward model rθ and calculate a score (i.e., reward), which represents the real-time feedback from the reward model. Next, we aims to maintain similarity between the RL model and the SFT model with Kullback-Leibler (KL) divergence. Finally, we combine the two loss functions as follows:
L rθ ϕ = E(x, y) ∼ DπRLϕ [rθ(x, y)], LSFTϕ = −log(πRLϕ (y|x)/πSFT (y|x)), LRL = w RL 1 · Lrθϕ + wRL2 · LSFTϕ ,
(11)
where πRLϕ (y|x) and πSFT (y|x) represent comments generated by RL model and the SFT model, respectively, wRL1 and w RL 2 are trainable weights.
4.4 Tree-of-Thought Refining To enhance the appeal of generated comments, we propose the knowledge-enhanced Tree-of-Thought (TOT) method which is inspired by Yao et al. (2023) as shown in Fig. 3.
We first construct TOT instructions to generate TOT content related to the video with GPT-4. TOT instructions in Fig. 3 (denoted as “ToTv”) include video description (denoted as “Des”, such as “A chef shares...”), key events (denoted as “Eve”, such as “Selecting ingredients, Making sauce, Cooking crepes.”), background knowledge (denoted as “Kno”, such as “French crepes are a traditional...”), creative associations (denoted as “Cre”, such as “Crepes are like a piece of art...”), and target audience (denoted as “Aud”, such as “People who love...”).
Next, we optimize the TOT content generated by GPT-4 with external knowledge bases like Wikidata in Fig. 3 (denoted as “ToTvn”). Specifically, i) we first link entities from the background knowledge in the TOT content to corresponding entities in knowledge graphs with TagMe (Ferragina and Scaiella, 2010). For example, the description from Wikidata of the “crepes” is “a European recipe originating from France, ...”. ii) Second, we detect if there are errors in the description of the linked entities. iii) Third, we enrich the background knowledge with GPT-4 that is to ask GPT-4 to expand the content with an instruction. Afterwards, we obtain the optimized video-related TOT content including “French crepes originated in the 13th century ...” as background knowledge and other four aspects maintain unchanged. iv) Fourth, we determinate weights for the optimized video-related TOT content. Knowledge-enhanced TOT is a tree where each content (i.e. each node) has a weight. We determine each weight’s order with GPT-4 based on video titles, captions, descriptions, and audio content. We introduce a utility function U(c,W T ) for calculating the utility of a comment as follows:
U(c,WT ) = 5∑
i=1
wTj · fTi (c), (12)
where c is a comment, W T represents the weight set for each TOT dimension, fTi (c) denotes the utility of comment c concerning the TOT’s ith dimension. Weights are optimized through gradient descent and iterated until convergence:
∂U(c,WT )
∂wTi = fTi (c), w
T ′ i = w T i − α×
∂U(c,WT )
∂wTi ,
(13)
HOTVCOM. Results of baselines are derived from being trained with our dataset.
where α as the learning rate. In this video, the weights for video description and creative association are much higher than others.
Then, we generate comment-related TOT content in Fig. 3 (denoted as “ToTc”) including comment description (denoted as “Des”’, such as “French crepes are delicious.”), key events (denoted as “Eve”’, such as “The taste of French crepes.”), background knowledge (denoted as “Kno”’), creative associations (denoted as “Cre”’, and target audience (denoted as “Aud”’).
After that, we optimize comment-related TOT content with optimized video-related TOT content in Fig. 3 (denoted as “ToTcn”). The optimized comment-related TOT content contains “French crepes originated in the 13th ...” as background knowledge, “Crepes are like a piece of art ...” as creative association, “People who love ...” as target audience. Other two aspects maintain unchanged.
Finally, we regenerate the comments with prompt engineering. The input is the best comment after RL (i.e.“The French crepes are very delicious”) and the optimized comment-related TOT content. The output is the best comment after TOT (i.e.“The French crepes are delicious food and like an artistic creation.”).
5 Experiments In this section, we conduct extensive experiments to evaluate the performance of our pro-
posed ComHeat framework in comparison to other baselines on generating hot-comments for
HOTVCOM.
5.1 Experimental Setups Our experiments are conducted on four Nvidia A100 GPUs, each with 80GB of memory, using PyTorch 4 in Python 5. For enhanced training efficiency, we utilize DeepSpeed. We set the maximum sequence length for both input and output sequences to maximum 1024 tokens. The training process is set to 10 epochs. We list detailed training hyperparameters in Table 10 in the Appendix.
5.2 Datasets, Baselines and Metrics We utilize four datasets and eleven baselines for comparison with details shown in Appendix D. All results are reported on the corresponding test sets or 20% subset split from the original dataset. For public datasets, including VideoIC, Livebot, and MovieLC, the tests are conducted directly on these public datasets without training. For our selfcollected datasets, which include HOTVCOM and the TikTok dataset, other baseline models have undergone training on our datasets.
We classify our metrics into two categories: popularity metrics and general metrics. Popularity metrics encompass informativeness, relevance, creativity, and user engagement. User engagement is essentially assessed through manual ratings from 1 to 5, where 1 means the worst and 5 means the best. The final scores will be scaled to 1- 100. We enroll three volunteers, and each of them is required to give scores for the randomly selected 500 videos with generated comments. We also calculate Inter-rater agreement of Krippendorff’s Alpha (IRA) to ensure the confidence of human ratings. For the controversial ratings which have low agreements (<0.7), we discard this com-
4https://pytorch.org/ 5https://www.python.org/
ment. General metrics encompass BLEU (Papineni et al., 2002), ROUGE (Lin, 2004), BLEURT (Sellam et al., 2020), COSMic (Inan et al., 2021), METEOR (Banerjee and Lavie, 2005) and selfCIDEr (Wang and Chan, 2019), which assess the relevance and the diversity of generated comments.
5.3 Main Results In the domain of Chinese video comment generation, ComHeat consistently outperforms prior methods, as shown in Tables 2, 3, 4, and 5. These evaluations span general, popularity, and human-centric metrics on the HOTVCOM dataset, as well as on the established datasets: Livebot, VideoIC, and MovieLC. Following the research by Sun et al. (2023b), we collect 1000 English videos from TikTok. Results, presented in Table 6, show that ComHeat maintains its effectiveness in an English video comment generation task, illustrating its cross-linguistic capabilities.
5.4 Ablation Study In Table 7, we analyze the impact of each ComHeat component. Excluding SFT (“w/o SFT”), RL (“w/o RL”), and TOT (“w/o TOT”) leads to the biggest drops in results, showing their importance in video comment generation. TOT is more effective than COT, as COT tends to produce longer comments. Basic and auxiliary rewards (“RW w/ br”, “RW w/ ar”) have similar effects. The results also show a preference for informativeness over creativity or knowledge (“w/o K”, “w/o I”, “w/o C”). The
knowledge-based correction (“w/o corr”) has limited influence. Overall, ComHeat performs better than all other setups, highlighting the combined strength of its parts. We also tested other backbones like MOSS-16B (Sun et al., 2023a), BELLE13B (Yunjie Ji, 2023; Yunjie Ji and Li, 2023), ChatGLM-6B (Du et al., 2022), and baichuan-13B, as seen in Fig. 4. In summary, backed by the baichuan2-13B model, ComHeat stands out, showing its potential in video comment generation.
5.5 Case Study
Examples of our proposed ComHeat’s performance are in the Appendix from Table 18 to Table 27 across various themes. We find that Unified Transformer, MML-CG, and KLVCG give basic comments, such as mentioning “cat food” in a query-like form, lacking directness. Our proposed ComHeat provides more detailed and emotional content. When compared to human comments, ComHeat has a different but engaging perspective, showing areas to improve. Comparing ComHeat and human generated comments as in Table 16, ComHeat is off-topic or too formal at times. Yet, its comments can be popular and resonate with some audiences due to their unique or funny nature, as shown in Table 17. This shows the great potential of ComHeat in adding variety to online comments.
6 Related Work 6.1 Video Comment Generation
Recent research predominantly emphasizes live or synchronized video comments like danmaku. Systems like GraspSnooker by Sun et al. (2019) and the rap-style generator by Jumneanbun et al. (2020) exemplify this trend. Major datasets like VideoIC by Wang et al. (2020) and innovations like the open-domain approach by Marrese-Taylor et al. (2022) have been introduced. While Chen et al. (2023) targets long videos, Ma et al. (2019) merges
visual and textual contexts. Our focus diverges towards generating alluring comments for full videos. Despite Sun et al. (2023b)’s contributions with the ViCo-20k dataset, they lack comprehensive engagement metrics. Notably, datasets like LiveBot (Ma et al., 2019) are live-comment centric, and Sun et al. (2023b)’s dataset, being English, might be less fitting for Chinese scenarios.
6.2 Video Caption Generation
Research on video caption generation has seen diverse approaches. Qi et al. (2023) launched GOAL, emphasizing Knowledge grounded Video Captioning (KGVC). Techniques like attention-based learning have been explored by Ji et al. (2022), while Song et al. (2022) introduce the Contextual Attention Network (CANet) for context-rich learning. Meanwhile, Yan et al. (2022) and Babavalian and Kiani (2023) offer unique frameworks for improved caption relevance and diversity. Yang et al. (2023b) propose a weighted semantic model, VMSG. Among multi-modal language model advancements, our work generate richer video descriptions, aiding in effective comment generation.
7 Conclusions and Future Work In conclusion, this study underscores the crucial role of “hot-comments” in enhancing video visibility, setting them apart from the prevalent “danmaku” comments. Through the introduction of the extensive Chinese video hot-comment dataset
HOTVCOM and the innovative ComHeat framework, we offer a novel approach to generate relevant and engaging video comments. In the future, we plan to ensure the ethics and fairness of the ComHeat framework from equal access and fair algorithms. We will provide low-cost or free tools and services, enabling smaller brands to enhance their video visibility. We also intend to include even more categories, especially those that
are more niche. Moreover, we will explore crosslingual perspectives for video comment generation.
Limitations While our research offers promising advancements in video hot-comment generation, it also presents certain limitations. First, our approach predominantly caters to Chinese short videos, which may constrain its applicability in diverse linguistic and cultural contexts. Second, the reliance on the ComHeat framework assumes that visual, auditory, and textual data are always present and of high quality, which might not always be the case in realworld scenarios. Furthermore, the optimization techniques employed, though effective, may not capture the full depth of human creativity. Lastly, while the dataset we introduce is comprehensive, it is inherently subject to the biases and characteristics of its source, potentially affecting the generalizability of our findings.
Ethic Statement Throughout the course of our research, we have maintained unwavering commitment to the highest ethical standards. Our foremost priorities include ensuring transparency, fairness, and the utmost respect for all participants involved in this study. We have taken extensive measures to safeguard user identities and protect privacy through a meticulous anonymization process applied to all data within our dataset. Our overarching objective is to enrich the user experience and interactions on video platforms while simultaneously upholding the principles of individual rights and human dignity. In a world where the influence of AI and technology continues to expand, we remain acutely aware of the profound impact these innovations can have on society as a whole. It is essential to acknowledge that in the realm of AI-driven comment generation, there exists the potential for harmful comments to emerge. Thus, we remain vigilant and resolute in our commitment to responsible research practices, with a strong emphasis on ethical considerations and societal well-being.
Acknowledgements This work is supported by Ant Group Research Intern Program, Science and Technology Commission of Shanghai Municipality Grant (No. 22511105902), Shanghai Municipal Science and Technology Major Project (No.2021SHZDZX0103), the National Natural Sci-
ence Foundation of China (No.62072323), Shanghai Science and Technology Innovation Action Plan (No. 22511104700), and the Zhejiang Lab Open Research Project (NO. K2022NB0AB04).
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A Details of data construction Specifically, in the OCR process, we use FFmpeg 6 to transform videos into uniform frame sequences. For enhanced caption accuracy, the OpenCV library 7 is employed, allowing dynamic adjustment of RGB thresholds, initially set at 127. Gaussian blurring, with parameters tailored to each video, is applied for noise mitigation. During ASR, FFmpeg again comes into play, isolating audio streams and converting them to linear 16 PCM format. To heighten speech extraction accuracy, the noisereduce library 8 is used, designating the first audio second as the noise benchmark. For keyframe extraction, OpenCV is utilized to derive color histograms and contours, and the Local Binary Pattern (LBP) algorithm (Pietikäinen, 2010) extracts texture features. After feature normalization, K-means clustering categorizes frames, with each cluster’s central frame chosen as the keyframe. The extraction’s precision is validated using SSIM (Wang
6https://ffmpeg.org/ 7https://opencv.org/ 8https://github.com/timsainb/noisereduce
et al., 2004) and PSNR measurements against the original video content, and 1% of videos undergo manual validation. For theme categorization, Video-ChatGPT discerns video content, while ChatGPT offers text insights. We randomly select 1% of videos for manually checking for accurate tagging.
B Exploratory Data Analysis We make detailed exploratory data analysis on the constructed HOTVCOM as illustrated in Fig. 5.
Fig. 5(a) shows the relationship between the maximum number of likes/replies and the number of comments. The X-axis represents the number of comments, and the Y-axis (logarithmic scale) represents the maximum number of likes and replies. The graph shows a trend of increasing maximum likes and replies as the number of comments increases.
Fig. 5(b) shows the average number of likes during different hours of the day. There are noticeable peaks in average likes during specific times, such as at 3 PM and 9 PM.
Fig. 5(c) shows the relationship between the number of fans and the average number of likes/replies. The X-axis represents the fan number ratio, and the Y-axis represents the average number of likes/replies. The chart shows that the average number of likes/replies initially increases with the number of fans but then decreases.
Fig. 5(d) shows the contribution percentage of users of different age groups to the most praised comment. It can be observed that certain age groups, such as those between 25 to 29 years old, have a particularly high contribution rate.
Fig. 5(e) shows the contribution percentage of different regions to the most praised comment. There is a significant variance in contributions by region, with some regions contributing much more than others.
Fig. 5(e) shows the average number of comments and the number of videos across different video categories. The X-axis represents the category of the video, the left Y-axis represents the average number of comments, and the right Y-axis represents the number of videos. It is evident that some categories, like “Finance and Economics”, have a high average number of comments, while others have a larger quantity of videos, such as “Knowledge”.
Combining the above insights, we can conclude that: i) Popular comments tend to increase in likes and replies with an increasing number of comments.
ii) Users are more active during certain periods, indicating peak times for liking videos. iii) Users with more fans tend to have comments that receive more likes and replies, although this trend is not linear. iv) Contributors to the most popular comments are more concentrated within certain age brackets. v) There is a significant regional difference in contributions to the most popular comments, suggesting that regional culture may affect comment interaction. vi) Different video categories perform differently in terms of attracting comments and the production of videos.
C More experiments Moreover, we introduce a dedicated dataset using video descriptions as labels to augment the information content of the generated captions. ComHeat’s performance, when compared to baselines in Chinese video comment generation, is evident in Table 8. This underlines ComHeat’s prowess in delivering contextually relevant Chinese captions. Furthermore, on the English video caption generation datasets MSVD (Chen and Dolan, 2011) and MSRVTT (Xu et al., 2016), as presented in Table 9, we list the results of the baselines based on the corresponding paper, and find ComHeat consistently excels, marking its robustness in bilingual caption generation tasks.
D Baselines We elaborate baselines as follows:
S2S-IC (Ma et al., 2019) integrates both visual and textual information. It utilizes two separate encoders to process images and comments. The
BLEU ROUGE BLEURT COSMIC CIDEr METEOR
UT 15.67 28.65 -0.47 50.12 44.69 8.33 MML-CG 22.70 35.08 -0.39 56.34 56.67 17.83 KLVCG 28.00 39.64 -0.37 58.92 62.83 22.55 ComHeat 41.21 58.19 -0.26 66.31 85.36 30.47 ↑ 13.21 18.55 0.11 7.39 22.53 7.92 ↑(%) 47.18 46.80 42.31 12.54 35.86 35.12
Table 8: The performance of ComHeat in comparison to other baselines in Chinese video comment datasets on the Chinese video caption dataset sourced from our proposed
HOTVCOM.
MSVD MSR-VTT BLEU ROUGE CIDEr METEOR BLEU ROUGE CIDEr METEOR
POSRL 53.90 72.10 91.00 34.90 41.30 62.10 53.40 28.70 ORG-TRL 54.30 73.90 95.20 36.40 43.60 62.10 50.90 29.70 O2NA 55.40 74.50 96.40 37.40 41.60 62.40 51.10 28.50 OA-BTG 56.90 - 90.60 36.20 41.40 - 46.90 28.20 GL-RG+IT 60.50 76.40 101.00 38.90 46.90 65.70 60.60 31.20 ComHeat 63.90 80.30 104.20 42.70 51.30 69.90 66.30 34.70 ↑ 3.40 3.90 3.20 3.80 4.40 4.20 5.70 3.50 ↑(%) 5.62 5.10 3.17 9.77 9.38 6.39 9.41 11.22
Table 9: The performance of ComHeat in comparison to other baselines in English caption datasets including MSVD and MSR-VTT.
outputs of these encoders are then concatenated and fed into an LSTM decoder to generate output comments.
FRNN (Ma et al., 2019), the Fusional RNN model, comprises three components: a video encoder, a text encoder, and a comment decoder. The video encoder processes a sequence of consecutive frames using an LSTM layer on top of a CNN layer, while the text encoder encodes surrounding live comments into vectors also using an LSTM layer. The comment decoder then generates the live comment based on these encodings.
UT/UT-ResNet (Ma et al., 2019), the unified transformer model, uses a linear structure to capture the dependencies between comments and videos. It consists of a video encoder, a text encoder, and a comment decoder.
UT-CLIP (Sun et al., 2023b) uses CLIP instead of ResNet for feature extraction based on the unified transformer model framework.
MML-CG (Wang et al., 2020), the Multimodal Multitask Learning framework for Comments Generation, first extracts different modality features using various encoders. These features are then integrated by a multimodal encoder, which jointly optimizes two tasks: temporal relation prediction and comment generation, in an end-to-end manner.
KLVCG (Chen et al., 2023) comprises independent modality encoders for video context, comment context, and external knowledge, along with a cross encoder and a decoder.
KLVCG+ (Chen et al., 2023) represents the results of KLVCG pre-trained on a mixture of Livebot, VideoIC, MovieLC datasets.
CLIP4C (Tang et al., 2021), the CLIP4Caption framework for video captioning, involves two stages of training. Initially, a video-text matching network is pre-trained on the MSR-VTT dataset for better visual representation. Then, this pre-trained network is used as a video feature extractor in the fine-tuning stage. The system inputs a sequence of frame embeddings to a video encoder, linked to a decoder that generates text. For ensemble purposes, multiple caption models with different layers of encoder and decoder are trained, and their outputs are combined for a robust final result.
GIT-2 (Wang et al., 2022) comprises a single image encoder and a text decoder, pre-trained on 0.8 billion image-text pairs with a language modeling task. It first uses a contrastive task to pre-train the image encoder, followed by a generation task to pre-train both the image encoder and text decoder.
ViCo-r, ViCo-u, and ViCo-f (Sun et al., 2023b) are sourced from ViCo. ViCo-r uses a generator
trained with randomly sampled comments, ViCou employs a generator trained with uniquenessguided sampling, and ViCo-f proposes a model called ViCo with three novel designs focusing on engagement quantification, automatic engagement evaluation, and alleviating scarcity of high-quality comments using reward feedback.
POSRL (Wang et al., 2019) consists of a gated fusion network, a POS sequence generator, and a description generator. It utilizes a self-critical sequence training method for reinforcement learning, focusing on exploiting relationships among different video features and POS tags of descriptions for generating comprehensive and accurate captions.
ORG-TRL (Zhang et al., 2020) follows an encoder-decoder framework, with an object relational graph at its core. It dynamically learns the interaction among different objects and attentively aggregates visual features to generate descriptions. The learning process includes both teacher-enforced and teacher-recommended strategies.
O2NA (Liu et al., 2021) is based on the Transformer decoder, comprises an object predictor, a length predictor, and two Transformer decoders, focusing on generating objects in parallel and then linking them to form fluent captions.
OA-BTG (Zhang and Peng, 2019) is an advanced video captioning model that follows an encoderdecoder framework. It begins by extracting frames and object regions from a video, constructing a bidirectional temporal graph to capture complex temporal dynamics. The model then aggregates these features into discriminative representations using learnable VLAD models, focusing on both local object details and global frame context. Finally, in the decoding stage, it integrates these representations and employs GRU units with hierarchical attention to generate detailed video descriptions, effectively balancing the contributions of multiple objects.
GL-RG+IT (Yan et al., 2022) is with an encoderdecoder architecture, includes a global-local encoder and a captioning decoder. It emphasizes aggregating different frame features to enrich globallocal vision representations and translates these into natural language sentences using an incremental training strategy.
E Metrics E.1 Annotation protocol
The manual metrics Fluency (short as Flue), Relevance (short as Rele), and Correctness (short as Corr) derive from Ma et al. (2019). In our study, we do not re-evaluate the Fluency (short as Flue), Relevance (short as Rele), and Correctness (short as Corr) for each model in Wang et al. (2020) and Ma et al. (2019); these scores are taken directly from the values published in their respective articles. We also list the corresponding content of three metrics as follows:
Fluency is designed to measure whether the generated live comments are fluent, setting aside their relevance to videos. Relevance is designed to measure the relevance between the generated live comments and the videos. Correctness is designed to synthetically measure the confidence that the generated live comments are made by humans in the context of the video. For all three aspects, we stipulate that the score should be an integer in {1, 2, 3, 4, 5}, with higher scores being better. Scores are evaluated by three human annotators, and we take the average of the three raters as the final result.
It is important to note that the human metrics defined in our paper include only user engagement. Informativeness, relevance, and creativity are automatic metrics. We have explained that user engagement represents the level of users’ interaction with a comment. It is essentially assessed through manual ratings from 1 to 5, where 1 means the
worst and 5 means the best. The final scores will be scaled to 1-100. Below, we also list the specific protocol as shown in Table 15.
E.2 Agreement scores among annotators We list the agreement scores for comments generated by each model and our method, ComHeat, on the proposed HOTVCOM (see Table 11), Livebot (see Table 13), VideoIC (see Table 14), and Tiktok (see Table 12) datasets. To facilitate easy reference, we retain the human evaluation scores and provide the corresponding agreement scores next to them.
F More cases We list more cases as shown from Table 18 to Table 27.
Score Description
Score 1 Disengaging: The comment is disengaging and irrelevant, failing to stimulate any noticeable user interaction. Score 2 Marginally Engaging: The comment is marginally engaging, eliciting only minimal user interaction. Score 3 Moderately Engaging: The comment is moderately engaging, sparking some interest and interaction. Score 4 Highly Engaging: The comment is highly engaging, resulting in significant user interaction. Score 5 Exceptionally Engaging: The comment is exceptionally engaging, generating substantial interest and high levels of user interaction.
Table 15: Annotation protocol of human evaluation.
Video
Theme 宠物 Pets 旅游 Travel 科技 Technology 时尚 Fashion
Title 感 谢 大 学 生！！！！ 就 是 怎 么 好 像 是 在 被 菠 萝 包 遛 啊#德 牧 #金毛 #大学生 在 抖 音 遛 到 狗 了 Thanks to college students! It feels like the dog is taking us for a walk, just like the "Pineapple Bun". #GermanShepherd #GoldenRetriever #StudentsWalkingDogOnTikTok 8848米， 珠 穆 朗 玛 峰 登 顶 视 角 ， 最 接 近 天 空 的 地 方 ， 神 奇 的 大 自 然#场 面 壮 观 #换个角 度 看 世 界 #极限 运动 #惊 险刺激 8848 meters, the view from the top of Mount Everest, the closest place to the sky, the magic of nature. #MagnificentView #DifferentPerspective #ExtremeSports #Thrilling 地 球 以 每 秒450米 的 速度自转， 深处地球的 我们为何感 觉不到？知 道 的 评 论 区. . . #探索 宇宙未解之 谜 The Earth rotates at a speed of 450 meters per second. Why can’t we, who are deep within the Earth, feel it? Comment if you know... 这 样 的 当 代 年 轻 人 新 型 生 存 法则，你们 觉得怎么样 呢 ？#消 费 降级 #年轻 人 #上海 What do you think of the new survival rules for modern young people? #ConsumerDowngrade #YoungPeople #Shanghai
ComHeat 大 学 生 遛 狗 ， 遛 出 了 孙 悟 空 遛 猴 子 的 感 觉！ College students walking the dog, feels like Sun Wukong walking the monkey! 珠 穆 朗 玛 峰 ， 一 座 高 耸 入 云 的 巨 人 ， 吸 引 着 人 们 去 攀 登 和 探 索。 Mount Everest, a giant towering into the clouds, attracts people to climb and explore. 我 以 每 小 时16公里的 速度开车， 为什么我感 觉不到 I drive at a speed of 16 kilometers per hour, why can’t I feel it? 在消费降级 的背景下， 上海年轻人 在降低消费 的同时保持 生活质量， 他们运用智 慧和创意找 到了享受生 活的方法。 这场智慧的 探险让人敬 佩，也给我 们带来了启 示。 In the context of consumption downgrade, young people in Shanghai maintain their quality of life while reducing consumption. They use wisdom and creativity to find ways to enjoy life. This journey of wisdom is admirable and enlightening.
Human 大 学 生 遛 完 回 来 就 这 样 打都 打不醒 After the college students walk, they can’t be woken up, even by hitting. 哇 ， 地 球好小 Wow, the Earth is so small. 我感觉不到 怪哉 I can’t feel it. How strange. 上海精致年 轻人会喝瑞 幸 Young refined Shanghai folks like to drink Luckin Coffee.
Table 16: Some cold comments generated by the proposed ComHeat.

heading: Future Work