Scalable Privacy-Compliant Virality Prediction on Twitter

The digital town hall of Twitter becomes a preferred medium of communication for individuals and organizations across the globe. Some of them reach audiences of millions, while others struggle to get noticed. Given the impact of social media, the question remains more relevant than ever: how to model the dynamics of attention in Twitter. Researchers around the world turn to machine learning to predict the most influential tweets and authors, navigating the volume, velocity, and variety of social big data, with many compromises. In this paper, we revisit content popularity prediction on Twitter. We argue that strict alignment of data acquisition, storage and analysis algorithms is necessary to avoid the common trade-offs between scalability, accuracy and privacy compliance. We propose a new framework for the rapid acquisition of large-scale datasets, high accuracy supervisory signal and multilanguage sentiment prediction while respecting every privacy request applicable. We then apply a novel gradient boosting framework to achieve state-of-the-art results in virality ranking, already before including tweet's visual or propagation features. Our Gradient Boosted Regression Tree is the first to offer explainable, strong ranking performance on benchmark datasets. Since the analysis focused on features available early, the model is immediately applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective Content Analysi

CASPR: Customer Activity Sequence-based Prediction and Representation

Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications. Our experiments at scale validate CASPR for both small and large enterprise applications.Comment: Presented at the Table Representation Learning Workshop, NeurIPS 2022, New Orleans. Authors listed in random orde

On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline

Our global population contributes visual content on platforms like Instagram, attempting to express themselves and engage their audiences, at an unprecedented and increasing rate. In this paper, we revisit the popularity prediction on Instagram. We present a robust, efficient, and explainable baseline for population-based popularity prediction, achieving strong ranking performance. We employ the latest methods in computer vision to maximize the information extracted from the visual modality. We use transfer learning to extract visual semantics such as concepts, scenes, and objects, allowing a new level of scrutiny in an extensive, explainable ablation study. We inform feature selection towards a robust and scalable model, but also illustrate feature interactions, offering new directions for further inquiry in computational social science. Our strongest models inform a lower limit to population-based predictability of popularity on Instagram. The models are immediately applicable to social media monitoring and influencer identification.Comment: Presented at ICAART 202

Redox-Active Glycol Nucleic Acid (GNA) Components: Synthesis and Properties of the Ferrocenyl-GNA Nucleoside, Phosphoramidite, and Semicanonical Dinucleoside Phosphate

Ferrocenylated glycol nucleic acid (Fc-GNA) components are rarely studied in the field of xeno nucleic acid (XNA) chemistry. As an attempt to contribute to XNA chemistry, in the present article we report a seven-step synthesis of the first semicanonical dinucleoside containing the Fc-GNA nucleoside linked to the adenosine nucleoside with a phosphodiester bond. First, the nucleoside-bearing ethynylferrocenyl moiety in the C5 position of the uracil nucleobase was obtained. In the following steps, the nucleoside was transformed into the phosphoramidite intermediate that in turn was reacted with N6-benzoyl-2′,3′-O-isopropylideneadenosine to afford the target dinucleoside phosphate with 47% yield. The newly obtained Fc-GNA nucleoside is redox-active, and on the basis of this property (function), it belongs to a new class of functional GNA (fun-GNA) nucleosides. The electrochemistry of the Fc-GNA nucleoside, dinucleoside phosphate, and ferrocenyl furanopyrimidone nucleoside that was obtained as an undesired byproduct of Fc-GNA nucleoside synthesis was investigated by cyclic voltammetry (CV). The CV result showed the presence of a one-electron ferrocenyl-centered redox wave in each case. The half-wave potentials of the Fc-GNA nucleoside and dinucleoside phosphate were 89 and 99 mV, respectively, against the FcH/FcH+ couple. Finally, the activity of the newly obtained Fc-GNA components was studied against the nontumorigenic mouse L929 and human cervix adenocarcinoma HeLa cells. The synthesized compounds showed no cytotoxic activity against the tested cell lines.K.K. thanks the National Science Center in Cracow, Poland (grant OPUS UMO-2018/29/B/ST5/00055), for financial support. Crystallographic measurements were carried out at the Biological and Chemical Research Centre, University of Warsaw, established within the project cofinanced by European Union from the European Regional Development Fund under the Operational Programme Innovative Economy, 2007–2013. The X-ray diffraction data were collected at the Core Facility for Crystallographic and Biophysical Research to support the development of medicinal products sponsored by the Foundation for Polish Science (FNP)

Organometallic ciprofloxacin conjugates with dual action: synthesis, characterization, and antimicrobial and cytotoxicity studies

The synthesis, characterization and biological activity of six bioorganometallic conjugates of ciprofloxacin with ferrocenyl, ruthenocenyl and cymantrenyl entities are described. Their antimicrobial activities were investigated against Gram-positive bacteria, Gram-negative bacteria and bloodstream forms of Trypanosoma brucei. Furthermore, the morphological changes of bacterial cells upon treatment with the conjugates were examined by scanning electron microscopy. In addition, the cytotoxicity of the conjugates against tumor and normal mammalian cells was also investigated. The results showed that conjugation of an organometallic moiety can significantly enhance the antimicrobial activity of the antibiotic ciprofloxacin drug. It was found that N-alkyl cymantrenyl and ruthenocenyl ciprofloxacin conjugates were the most effective derivatives although other conjugates also showed significant antimicrobial activity. The increase in the antimicrobial activity was most likely due to two independent mechanisms of action. The first mechanism is due to the bacterial topoisomerase inhibitory activity of ciprofloxacin while the second mechanism can be attributed to the generation of reactive oxygen species caused by the organometallic moiety. The presence of two modes of action enables the conjugates to kill bacteria in their stationary growth phase and to overcome the drug resistance of S. aureus strains. In addition, the conjugates showed promising selectivity toward bacterial and parasitic cells over mammalian cells

The complexity of social media response:Statistical evidence for one-dimensional engagement signal in Twitter

Many years after online social networks exceeded our collective attention, social influence is still built on attention capital. Quality is not a prerequisite for viral spreading, yet large diffusion cascades remain the hallmark of a social influencer. Consequently, our exposure to low-quality content and questionable influence is expected to increase. Since the conception of influence maximization frameworks, multiple content performance metrics became available, albeit raising the complexity of influence analysis. In this paper, we examine and consolidate a diverse set of content engagement metrics. The correlations discovered lead us to propose a new, more holistic, one-dimensional engagement signal. We then show it is more predictable than any individual influence predictors previously investigated. Our proposed model achieves strong engagement ranking performance and is the first to explain half of the variance with features available early. We share the detailed numerical workflow to compute the new compound engagement signal. The model is immediately applicable to social media monitoring, influencer identification, campaign engagement forecasting, and curating user feeds.Comment: Presented at ICAART 202