## 1 Introduction  
Citation analysis has been an active area of research to measure the impact of scientific publications [1; 2; 3; 4]. Since the inception of the internet, interactions and communication among scholars around the world have expanded. This presents the concept of knowledge flow, which is the exchange of knowledge among the different scientific communities [5; 6; 7]. Researchers from similar research areas tend to be interested in the same articles. Therefore, many techniques have used citation-based networks to improve document recommendation systems [8; 9; 10]. [PERSON] et al. [11] addressed the problems of high dimensionality input data and real-time consumption by introducing a scalable subspace clustering algorithm (SCuBA) approach. They tested the proposed algorithm on the data corpus from MovieLens (movie ratings by millions of users) and concluded that the approach outperforms subsequent clustering techniques (i.e., fallback models) at 15% precision, and is also faster, scalable, and produces high-quality recommendations. Similarly, [PERSON] and [PERSON] [12] used citation-based graphs to construct a research article recommendation system. They proposed a PageRank algorithm that combines the properties of a citation graph and random walk. The algorithm assigns a preference score to a set of publications and linked documents based on their bibliographic references. They experimented with nine datasets and observed that the model ranked the documents in every dataset on a citation graph at the first 20 positions. [PERSON] et al. [13] designed a search-based recommendation system to find the most recent and relevant documents in response to a user query. They suggested a direction-aware network where each edge has a citation direction, using the dataset from CiteSeer and DBLP and comparing the proposedtechnique with the page rank algorithm. According to the results, PaperRank leads to higher accuracy when the query is generic, whereas a direction-aware method performs better when the query is specific. Some studies present prototypes that allow the user to visualize the citation network in terms of how other documents use a certain document. [PERSON] et al. [14] presented the \"Cite2 vec\" visualization scheme that permits users to browse documents by using word embedding. In contrast to the above approaches, this technique considers contextual information, as well as the publication venue and author details. Another study, by [PERSON] and [PERSON] [15], presented \"Paper2 vec\", an approach focused on the neighboring nodes of publications in a citation network. [PERSON] and [PERSON] [16] incorporated author information for context-aware recommendations by employing a neural citation network. In this paper, we designed a citation context classification approach using customized focal-loss and class-weight-aware convolutional neural network (CNN) network. This type of technology can be applied for mining social media structured and semi-structured data sources as well [17; 18; 19; 20; 21]. We propose that visualizing scholarly research influence can capture multiple perspectives that provide both qualitative and quantitative data. Visualizations are frequently utilized to engage both novice and experienced users. Visualizations can help people and researchers to understand patterns and correlations in datasets and serve as storytelling tools. A well-designed visualization can also assist analysis in varying degrees of detail, ranging from offering a gestalt perspective of a scholarly research study to its dissemination into different disciplines. The primary contribution of this paper is a broadly accessible, automated approach to visualizing the scientific research dissemination over time. In this paper, we measured the scientific research dissemination by analyzing the Association of Computational Linguistics (ACL) data corpus. We designed a novel focal loss and weight-aware deep neural network for citation context classification.