Exploring Time-Sensitive Variational Bayesian Inference LDA for Social Media Data

There is considerable interest among both researchers and the mass public in understanding the topics of discussion on social media as they occur over time. Scholars have thoroughly analysed sampling-based topic modelling approaches for various text corpora including social media; however, another LDA topic modelling implementation—Variational Bayesian (VB)—has not been well studied, despite its known efficiency and its adaptability to the volume and dynamics of social media data. In this paper, we examine the performance of the VB-based topic modelling approach for producing coherent topics, and further, we extend the VB approach by proposing a novel time-sensitive Variational Bayesian implementation, denoted as TVB. Our newly proposed TVB approach incorporates time so as to increase the quality of the generated topics. Using a Twitter dataset covering 8 events, our empirical results show that the coherence of the topics in our TVB model is improved by the integration of time. In particular, through a user study, we find that our TVB approach generates less mixed topics than state-of-the-art topic modelling approaches. Moreover, our proposed TVB approach can more accurately estimate topical trends, making it particularly suitable to assist end-users in tracking emerging topics on social media

Evaluating Similarity Metrics for Latent Twitter Topics

Topic modelling approaches such as LDA, when applied on a tweet corpus, can often generate a topic model containing redundant topics. To evaluate the quality of a topic model in terms of redundancy, topic similarity metrics can be applied to estimate the similarity among topics in a topic model. There are various topic similarity metrics in the literature, e.g. the Jensen Shannon (JS) divergence-based metric. In this paper, we evaluate the performances of four distance/divergence-based topic similarity metrics and examine how they align with human judgements, including a newly proposed similarity metric that is based on computing word semantic similarity using word embeddings (WE). To obtain human judgements, we conduct a user study through crowdsourcing. Among various insights, our study shows that in general the cosine similarity (CS) and WE-based metrics perform better and appear to be complementary. However, we also find that the human assessors cannot easily distinguish between the distance/divergence-based and the semantic similarity-based metrics when identifying similar latent Twitter topics

A customisable pipeline for continuously harvesting socially-minded Twitter users

On social media platforms and Twitter in particular, specific classes of users such as influencers have been given satisfactory operational definitions in terms of network and content metrics. Others, for instance online activists, are not less important but their characterisation still requires experimenting. We make the hypothesis that such interesting users can be found within temporally and spatially localised contexts, i.e., small but topical fragments of the network containing interactions about social events or campaigns with a significant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profile discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profiles dataset includes key network and content-based users metrics, enabling experimentation with user-defined score functions that characterise specific classes of online users. The paper describes the design and implementation of the pipeline and its empirical evaluation on a case study consisting of healthcare-related campaigns in the UK, showing how it supports the operational definitions of online activism, by comparing three experimental ranking functions. The code is publicly available.Comment: Procs. ICWE 2019, June 2019, Kore

Synthesis and electromagnetic wave absorption property of amorphous carbon nanotube networks on a 3D graphene aerogel/BaFe12O19 nanocomposite

Homogeneous amorphous carbon nanotube (ACNT) networks have been synthesized using floating catalyst chemical vapor deposition method on a 3D graphene aerogel (GA)/ BaFe12O19 (BF) nanocomposite which was prepared by a self-propagating combustion process. The as-synthesized ACNT/GA/BF nanocomposite with 3D network structures could be directly used as a good absorber material for electromagnetic wave absorption. The experimental results indicated that the minimum reflection loss of ACNT/GA/BF composite with a thickness of 2 mm was -18.35 dB at 10.64 GHz in the frequency range of 2-18 GHz. The frequency bandwidth of the reflection loss below -10 dB was 3.32 GHz and below -5 dB was 6.24 GHz, respectively. The 3D graphene aerogel structures which composed of dense interlined tubes and amorphous structure bearing quantities of dihedral angles could consume the incident waves through multiple reflection and scattering inside the 3D web structures. The interlinked ACNTs have both the virtues of amorphous CNTs (multiple reflection inside the wall) and crystalline CNTs (high conductivity), consuming the electromagnetic wave as resistance heat. ACNT/GA/BF composite has a good electromagnetic wave absorption performance.Institute of Textiles and Clothing2016-2017 > Academic research: refereed > Publication in refereed journalbcr

In situ evidence for the structure of the magnetic null in a 3D reconnection event in the Earth's magnetotail

Magnetic reconnection is one of the most important processes in astrophysical, space and laboratory plasmas. Identifying the structure around the point at which the magnetic field lines break and subsequently reform, known as the magnetic null point, is crucial to improving our understanding reconnection. But owing to the inherently three-dimensional nature of this process, magnetic nulls are only detectable through measurements obtained simultaneously from at least four points in space. Using data collected by the four spacecraft of the Cluster constellation as they traversed a diffusion region in the Earth's magnetotail on 15 September, 2001, we report here the first in situ evidence for the structure of an isolated magnetic null. The results indicate that it has a positive-spiral structure whose spatial extent is of the same order as the local ion inertial length scale, suggesting that the Hall effect could play an important role in 3D reconnection dynamics.Comment: 14 pages, 4 figure

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Identification of Autotoxic Compounds in Fibrous Roots of Rehmannia (Rehmannia glutinosa Libosch.)

Rehmannia is a medicinal plant in China. Autotoxicity has been reported to be one of the major problems hindering the consecutive monoculture of Rehmannia. However, potential autotoxins produced by the fibrous roots are less known. In this study, the autotoxicity of these fibrous roots was investigated. Four groups of autotoxic compounds from the aqueous extracts of the fibrous roots were isolated and characterized. The ethyl acetate extracts of these water-soluble compounds were further analyzed and separated into five fractions. Among them, the most autotoxic fraction (Fr 3) was subjected to GC/MS analysis, resulting in 32 identified compounds. Based on literature, nine compounds were selected for testing their autotoxic effects on radicle growth. Seven out of the nine compounds were phenolic, which significantly reduced radicle growth in a concentration-dependent manner. The other two were aliphatic compounds that showed a moderate inhibition effect at three concentrations. Concentration of these compounds in soil samples was determined by HPLC. Furthermore, the autotoxic compounds were also found in the top soil of the commercially cultivated Rehmannia fields. It appears that a close link exists between the autotoxic effects on the seedlings and the compounds extracted from fibrous roots of Rehmannia