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1. Proactively Identifying Emerging Hacker Threats from the Dark Web: A Diachronic Graph Embedding Framework (D-GEF)

   https://doi.org/10.1145/3409289  

   Samtani, Sagar ; Zhu, Hongyi ; Chen, Hsinchun ( August 2020 , ACM Transactions on Privacy and Security)

   null (Ed.)

   Cybersecurity experts have appraised the total global cost of malicious hacking activities to be $450 billion annually. Cyber Threat Intelligence (CTI) has emerged as a viable approach to combat this societal issue. However, existing processes are criticized as inherently reactive to known threats. To combat these concerns, CTI experts have suggested proactively examining emerging threats in the vast, international online hacker community. In this study, we aim to develop proactive CTI capabilities by exploring online hacker forums to identify emerging threats in terms of popularity and tool functionality. To achieve these goals, we create a novel Diachronic Graph Embedding Framework (D-GEF). D-GEF operates on a Graph-of-Words (GoW) representation of hacker forum text to generate word embeddings in an unsupervised manner. Semantic displacement measures adopted from diachronic linguistics literature identify how terminology evolves. A series of benchmark experiments illustrate D-GEF's ability to generate higher quality than state-of-the-art word embedding models (e.g., word2vec) in tasks pertaining to semantic analogy, clustering, and threat classification. D-GEF's practical utility is illustrated with in-depth case studies on web application and denial of service threats targeting PHP and Windows technologies, respectively. We also discuss the implications of the proposed framework for strategic, operational, and tactical CTI scenarios. All datasets and code are publicly released to facilitate scientific reproducibility and extensions of this work. 

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2. A deep semantic matching approach for identifying relevant messages for social media analysis

   https://doi.org/10.1038/s41598-023-38761-y  

   Biggers, Frederick Brown ; Mohanty, Somya D. ; Manda, Prashanti ( July 2023 , Scientific Reports)

   There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet. This paper seeks to demonstrate a way to present the changing semantics of Twitter within the context of a crisis event, specifically tweets during Hurricane Irma. These methods can be used to identify the most relevant corpus of text for analysis in relevance to a specific incident such as a hurricane. Using an implementation of the Word2Vec method of Neural Network training mechanisms to create Word Embeddings, this paper will: discuss how the relative meaning of words changes as events unfold; present a mechanism for scoring tweets based upon dynamic, relative context relatedness; and show that similarity between words is not necessarily static. We present different methods for training the vector model in Word2Vec for identification of the most relevant tweets for any search query. The impact of tuning parameters such as Word Window Size, Minimum Word Frequency, Hidden Layer Dimensionality, and Negative Sampling on model performance was explored. The window containing the local maximum for AU_ROC for each parameter serves as a guide for other studies using the methods presented here for social media data analysis.

    

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3. Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts

   https://doi.org/10.1145/3539597.3570475  

   Zhang, Yu ; Zhang, Yunyi ; Michalski, Martin ; Jiang, Yucheng ; Meng, Yu ; Han, Jiawei ( February 2023 , ACM)

   Proceedings of the Sixteenth (Ed.)

   Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user’s interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seedguided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches. 

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4. The Specificity of Sound Symbolic Correspondences in Spoken Language

   https://doi.org/10.1111/cogs.12474  

   Tzeng, Christina Y. ; Nygaard, Lynne C. ; Namy, Laura L. ( December 2016 , Cognitive Science)

   Although language has long been regarded as a primarily arbitrary system,sound symbolism, or non‐arbitrary correspondences between the sound of a word and its meaning, also exists in natural language. Previous research suggests that listeners are sensitive to sound symbolism. However, little is known about the specificity of these mappings. This study investigated whether sound symbolic properties correspond to specific meanings, or whether these properties generalize across semantic dimensions. In three experiments, native English‐speaking adults heard sound symbolic foreign words for dimensional adjective pairs (big/small, round/pointy, fast/slow, moving/still) and for each foreign word, selected a translation among English antonyms that either matched or mismatched with the correct meaning dimension. Listeners agreed more reliably on the English translation for matched relative to mismatched dimensions, though reliable cross‐dimensional mappings did occur. These findings suggest that although sound symbolic properties generalize to meanings that may share overlapping semantic features, sound symbolic mappings offer semantic specificity.

    

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5. A New Approach to Animacy Detection

   Jahan, Labiba ; Chauhan, Geeticka ; Finlayson, Mark A ( August 2018 , Proceedings of the 27th International Conference on Computational Linguistics)

   Animacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, co-reference resolution, semantic role labeling, and others. Prior work treated animacy as a word-level property, and has developed statistical classifiers to classify words as either animate or inanimate. We discuss why this approach to the problem is ill-posed, and present a new approach based on classifying the animacy of co-reference chains. We show that simple voting approaches to inferring the animacy of a chain from its constituent words perform relatively poorly, and then present a hybrid system merging supervised machine learning (ML) and a small number of hand-built rules to compute the animacy of referring expressions and co-reference chains. This method achieves state of the art performance. The supervised ML component leverages features such as word embeddings over referring expressions, parts of speech, and grammatical and semantic roles. The rules take into consideration parts of speech and the hypernymy structure encoded in WordNet. The system achieves an F1 of 0.88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0.75 for classifying the animacy of coreference chains themselves. We release our training and test dataset, which includes 142 texts (all narratives) comprising 156,154 words, 34,698 referring expressions, and 10,941 co-reference chains. We test the method on a subset of the OntoNotes dataset, showing using manual sampling that animacy classification is 90% +/- 2% accurate for coreference chains, and 92% +/- 1% for referring expressions. The data also contains 46 folktales, which present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, trees that talk). We show that our system is able to detect the animacy of these unusual referents with an F1 of 0.95. 

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