Keyword expansion techniques for mining social movement data on social media

Bozarth, Lia; Budak, Ceren

doi:10.1140/epjds/s13688-022-00343-9

Cited by 8 publications

(10 citation statements)

References 46 publications

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“…We also proved that the union of the expanded word lists does not yield to better results. These evidences are in contrast with the ones of [17], which are relative to the amount of relevant tweets key word expansion algorithms can retrieve. Indeed, the focus of the two papers is different as our analysis addresses a different research question and does not deal with the problem of text mining on Twitter.…”

Section: Discussionmentioning

confidence: 75%

“…While informative, the work on Lexifield is based on a narrow set of methods (word embedding-based and knowledge-based approaches) and on a limited set of topics (sound, taste and odour). Another effort towards the creation of a baseline is constituted by [17]. There, the authors investigate the problem in relation to the retrieval of tweets.…”

Section: Introductionmentioning

confidence: 99%

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LEXpander: applying colexification networks to automated lexicon expansion

Di Natale,

Garcia

2022

Preprint

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Background / IntroductionRecent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents.These lists are often generated by applying computational lexicon expansion methods to small, manually-curated sets of root words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. MethodsIn this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e. semantic networks connecting words based on shared concepts and translations to other languages. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on various word embedding models and synonym networks. ResultsWe find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various corpora. ConclusionThis way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.

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Section: Discussionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

LEXpander: applying colexification networks to automated lexicon expansion

Di Natale,

Garcia

2022

Preprint

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show abstract

“…However, the high recall can benefit applications to a different range of problems, as for example in the area of text mining. In particular, Bozarth and Budak (2022) highlight that when considering the amount of relevant tweets that keyword expansion algorithms can retrieve, the union of expanded lexica leads to better results than the ones achieved by the single word lists.…”

Section: Discussionmentioning

confidence: 99%

“…While informative, the work on Lexifield is based on a narrow set of methods (word embedding-based and knowledge-based approaches) and on a limited set of topics (sound, taste, and odor). Another effort towards the creation of a baseline is constituted by Bozarth and Budak (2022). There, the authors investigate the problem in relation to the retrieval of tweets.…”

Section: Introductionmentioning

confidence: 99%

LEXpander: Applying colexification networks to automated lexicon expansion

Natale

García

2023

Behav Res

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Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.

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“…Another problem with DHS keywords is that they are non-lexical; therefore, exact matches result from bypassing meaningful or related words. As a result, pipelined keyword enhancing technique is implemented in [31] by integrating seed keywords with pipelines. In their work, the authors mentioned that the most relevant words with a particular topic are identified as seed keywords.…”

Section: Figure 1 Working Of Hash Function and Bitwise Function In Me...mentioning

confidence: 99%

SDOT: Secure Hash, Semantic Keyword Extraction, and Dynamic Operator Pattern-Based Three-Tier Forensic Classification Framework

Joseph

Viswanathan

2023

IEEE Access

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Most traditional digital forensic techniques identify irrelevant files in a corpus using keyword search, frequent hashes, frequent paths, and frequent size methods. These methods are based on Message Digest and Secure Hash Algorithm-1, which result in a hash collision. The threshold criteria of files based on frequent sizes will lead to imprecise threshold values that result in an increased evaluation of irrelevant files. The blacklisted keywords used in forensic search are based on literal and non-lexical, thus resulting in increased false-positive search results and failure to disambiguate unstructured text. Due to this, many extraneous files are also being considered for further investigations, exacerbating the time lag. Moreover, the non-availability of standardized forensic labeled data results in (O(2 n )) time complexity during the file classification process. This research proposes a three-tier Keyword Metadata Pattern framework to overcome these significant concerns. Initially, Secure Hash algorithm-256 hash for the entire corpus is constructed along with custom regex and stop-words module to overcome hash collision, imprecise threshold values, and eliminate recurrent files. Then blacklisted keywords are constructed by identifying vectorized words that have proximity to overcome traditional keyword search's drawbacks and to overcome false positive results. Dynamic forensic relevant patterns based on massive password datasets are designed to search for unique, relevant patterns to identify the significant files and overcome the time lag. Based on tier-2 results, files are preliminarily classified automatically in O(log n) complexity, and the system is trained with a machine learning model. Finally, when experimentally evaluated, the overall proposed system was found to be very effective, outperforming the existing two-tier model in terms of finding relevant files by automated labeling and classification in O(nlog n) complexity. Our proposed model could eliminate 223K irrelevant files and reduce the corpus by 4.1% in tier-1, identify 16.06% of sensitive files in tier-2, and classify files with 91% precision, 95% sensitivity, 91% accuracy, and 0.11% Hamming loss compared to the two-tier system.

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Keyword expansion techniques for mining social movement data on social media

Cited by 8 publications

References 46 publications

LEXpander: applying colexification networks to automated lexicon expansion

LEXpander: applying colexification networks to automated lexicon expansion

LEXpander: Applying colexification networks to automated lexicon expansion

SDOT: Secure Hash, Semantic Keyword Extraction, and Dynamic Operator Pattern-Based Three-Tier Forensic Classification Framework

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