text:
\section{Introduction}

The availability of large amount of data sources and the emergence of various social networks including Twitter and Reddit, increased the social connectivity of people. This allowed them to easily express, propagate, share and dispute opinions and gives us a great opportunity to study and understand social phenomena like controversial topics.
Expressed opinions through posts and articles often trigger fierce and sometimes endless debates, and frequently cause a controversy.
A controversial content can  simply be defined as any content that attracts both positive and negative feedback~\cite{Hessel2019Controversy}. Indeed, polarization stigmatizes user's behavior in presence of controversial topics~\cite{Beelen2017NewsControversy, Garimella2018ReducingControversy, Garimella2017Quantifying}.

Automatic controversy detection can be helpful. For instance, people can be warned by the existence of a controversy to add some nuance to better understand some issues. Objective information could  also be brought to people to prevent misinformation or hateful discussions~\cite{Garimella2018ReducingControversy}. Detecting a content as non controversial is also helpful as it shows that people agree on a given issue.

Automatic controversy identification is difficult and constitutes a challenging task as it should be done on a large number of continuously evolving posts covering a wide range of topics.
 This difficulty is increased by the fact that controversy is sometimes time-aware (what is controversial today was not necessary controversial in the past) and community-aware (what is controversial in a community is not necessary controversial in another community)~\cite{Jang2017ControversiInPopulation}.
\newline
Controversy analysis on web pages or articles is usually based on features extracted  from the content~\cite{Sznajder2019Controversy, Jang2016ProbabilisticAT}.
However, on social media, the interaction between people is widely used to detect controversy.
These interactions include social relation (retweet and follow on Twitter, comment on Reddit)~\cite{Garimella2017Quantifying, Morales2015PoliticalPolarization}  and citation relation on Wikipedia~\cite{Jang2016ImproveAutomatedCD}.

In this paper, we focus on detecting controversy on the social media Reddit, even if any other social media (Twitter, Facebook, etc.) can  also be used after making few adaptations of the graph building stage.
The originality of our approach firstly resides on using very recent state-of-the-art \textsc{gnn} methods to embed user nodes in a low d-dimensional space and take into account structural information.
Initial text representations of a user are learnt from their messages on a current post and used as input of our \textsc{gnn}-based approach. To the best of our knowledge, only Zhong et al. started to use \textsc{gnn}s,
building their post-level controversy detection method by directly exploiting the comment-tree structure~\cite{Zhong2020ControversyGCN}.
Our work, on the contrary, exploits the user's interaction graph built from the comment-tree structure and
compare multiple state-of-the art (\textsc{gnn}s) to combine both
structural and content information.

To detect controversial posts, we propose a \textsc{gnn}-based approach which
consists of the following main contributions:
\begin{itemize}

\item  \textsc{gnn} architecture-based controversy detection. Our controversy detection is based on a graph classification.
We propose two strategies to embed the whole graph structure.
The first strategy aims to exploit hierarchical structure that may exist in the user graph structure.
Graph information are aggregated across the edges iteratively and in a hierarchical way. In our work, we rely on the \textsc{diffpool} approach which encodes the whole graph by stacking several pooling layers~\cite{Ying2018Diffpool}.
The second strategy is based on an attention mechanism. It aims to allow each user node to
judge which user neighbor is more or less important than the others, during the node
embedding process, according to the structure and features of the graph.

\item Experimental study. We conduct experiments on real-world datasets to evaluate the proposed
\textsc{gnn}-based approach only using structural information.
We show that our approach gets good performance compared to our baseline.

\item Textual features. We show that incorporating initial textual representation of users can improve the performance of our approach.

\end{itemize}
The rest of this paper is organized as follows.
Section 2 provides some related work.
Section 3 presents an overview of our approach to automatically detect controversy on social media.
Its different stages are described and formalized.
Section 4 presents the performance experiments and discusses the obtained results.
Section 5 concludes the paper and highlights some future work.

\section{Related Work}
Works on controversy analysis can be classified in three groups: content-based, structure-based and hybrid approaches.

\textbf{Content-based.} Early methods to detect controversy are mainly based on textual features, and only focus on language semantic, supposing that immediate textual context of concept can be highly indicative \cite{Sznajder2019Controversy} or that text-content can be used as a tool for detecting controversial topic/post.
Several studies focus on the web controversy, thanks to sources like Wikipedia, where pages can automatically be labelled as controversial, using "edit-wars" \footnote{multiple editors on a Wikipedia concept exchanging opposing opinions.} and relations/citations between pages.
In~\cite{Sznajder2019Controversy},  an approach to measure how controversial a concept is on Wikipedia pages is proposed.
Instead of relying on Wikipedia's metadata, authors argue that immediate textual context of a concept is strongly indicative of controversiality.
They represent articles via pre-trained word embeddings methods
and define three controversiality estimators based on the nearest neighbors, naive Bayes, and recurrent neural network respectively.
In~\cite{Dori2016Collective}, authors were interested in identifying whether a given content on a web page is controversial or not.
The collective controversy classification model is based on a nearest neighbor classifier that identifies an article according to the related Wikipedia articles.
The idea is that if related Wikipedia articles are controversial, it is likely that the article is also controversial.
Other studies focus on probabilistic approaches~\cite{Jang2016ImproveAutomatedCD, Jang2016ProbabilisticAT} to combine Wikipedia Controversy meta-data and features like the \textit{MCD} score\footnote{presence of certain words,  ferocity of "edit-wars", etc.}.
Articles, from web media, are also a huge source of information.

\textbf{Structure-based.}
Textual messages on social media are usually biased and meanings might be different depending on many factors, such as the culture or language of the communities, and therefore should be treated with precaution.
When studying controversy on a user interaction basis, the structural information of those interactions become particularly relevant, especially on social media.
Each social media has its own code. For example, Twitter has specific features, such as 'Retweet'
and 'Follow'.
In~\cite{Garimella2017Quantifying}, a user hybrid graph, combining follow and retweet edges, is built for a topic, which is defined by a set of hashtags. After partitioning the graph on two distinct communities, different methods to measure the controversy are checked, including a random-walk-based controversy measure (RWC).
In a similar study \cite{Garimella2018ReducingControversy}, they use the same graph to quantify and reduce controversy, by connecting opposing sides and creating bridges between communities for more exposure.
In \cite{Emamgholizadeh2020BRWForQC}, a similar approach is used suggesting that we can level user commitment at their community by looking at their relation. They propose a new method using Biased Random-Walk and adapt a new controversy measure to quantify.
A previous research focused on more exposed node boundaries, with statistical polarization measures to evaluate controversy \cite{Guerra2013comBoundariesPolarization}.
In \cite{Mendoza2020PolarizationGENE}, the importance of users and named entities involved in a discussion are highlighted. They generate a conditioned graph on named entities partitioned, and quantify controversy using a RWC (Random-Walk Controversy) score.
Even if structural features are widely covered and seem to be a strong asset, not covering text features appears to be a huge loss of relevant information.

\textbf{Hybrid methods.}
Recent studies focus on combining both structural and content information to avoid losing valuable features.
On this condition, Social media appears to be the ideal source, with the multiplicity of user interactions.
In~\cite{Dezarate2020VocQuantifyingControversy}, authors extend the work of Garimella and al.\cite{Garimella2017Quantifying} and propose a vocabulary-based controversy detection.
Using the partitioned user graph, tweets of the two selected communities are grouped by users, pre-processed, concatenated and labeled by the community name of their corresponding user.
They constitute the dataset which is used to train the text representation model FastText.
The controversy score is finally computed by using the embedding of the central users.
In~\cite{Hessel2019Controversy}, authors demonstrate that mixing
structural features (number of comments, max depth/total comment ratio, average node depth, etc.) of post-comment tree of a Reddit discussion with textual features outputted by language models such as BERT \cite{Devlin2018BERT} can improve predictive performance of early controversy post-level detection.
With the same objective, a Graph Convolutional Network based approach is proposed in~\cite{Zhong2020ControversyGCN} by Zhong et al. It aims to integrate information extracted from the comment-tree structure as well as content of post and its comments. A parallel multi-task classifier is added on their model to disentangle topic-related and topic-unrelated features for inter-topic detection.
Even with good performance, this approach presents some limits. The comment-tree structure of a post prevents us from exploiting user interactions, and the use of inter-topics relation might interfere too much with the main detection task. However, to the best of our knowledge, this is the first study which focuses on \textsc{gnn} for controversy analysis.\\
We present in this paper a new hybrid controversy detection approach, based on user graph interaction and state-of-the-art \textsc{gnn}s to combine valuable textual and structure information.

\section{Graph Neural Network-based Controversy Detection Approach}\label{controversydetectionapproach}

This section describes our  post-level controversy detection approach. It focuses on the  Reddit social media. Any other social media can be used with very few adaptations of the graph building stage.
The main idea is to exploit both text content and user interactions by representing the Reddit discussion as a user graph and exploring advanced \textsc{gnn} embedding techniques.
 Figure~\ref{fig:pipeline_apporach} presents an overview of our approach. We divide our pipeline into four sequential stages: Graph Building, User Feature Extraction, Graph Embedding, and Graph Classification.

The graph building stage represents data extracted from the Reddit social media as a user graph.
We represent the initial comment-post tree as a graph where nodes represent users and edges correspond to the interaction that exists between users.
Each node is represented by its own data (user-id, age, location, texts, etc.).
The user feature extraction stage enriches graph nodes by adding textual embedded features. These features are computed by using state-of-the-art NLP techniques. This allows to better interpret the texts that users sent out.
The graph embedding stage computes the embedding of the whole graph.
Different advanced \textsc{gnn}-based graph representation learning techniques are used, namely \textsc{diffpool} \cite{Ying2018Diffpool}, \textsc{gcn} \cite{Kipf2017GCN},
and \textsc{gat-gc} \cite{Zhang2020GATGC}.
Finally, the graph classification stage predicts the binary label associated to the whole graph, that is to classify a post as controversial or not.

\begin{figure*}[!htbp]
	\centering
		\includegraphics[width=1\textwidth]{images/pipeline_paper.png}
	\caption{Overview of our controversy detection approach.}
	\label{fig:pipeline_apporach}
\end{figure*}

\begin{figure*}[!htbp]
	\centering
		\includegraphics[width=0.7\textwidth]{images/reddit_graph_screen.pdf}
	\caption{Left: User graph of a controversial post. Right: User graph of a non-controversial post. The more interaction there is between two nodes, the bolder the link is.}
	\label{fig:user_graph_reddit}
\end{figure*}

\subsection{Graph building}

Existing controversy detection methods~\cite{Hessel2019Controversy, Zhong2020ControversyGCN} on Reddit use the classic comment-post tree representation as they mainly focus on the structure of the discussion.
However, many research works have established that user interaction can be helpful to extract different features on social media that can improve the controversy detection.
In this work, we adopt a graph representation of a discussion to highlight these user interactions.
Given a discussion on a post (thread) \(p\) extracted from a subreddit \(s\), we build an undirected graph where a node \(u_i\) represents a user involved in the discussion. An edge \((u_i,u_j)\) is created when a user \(u_j\) responds to the post $p$ or any comment posted by \(u_i\).
Figure~\ref{fig:user_graph_reddit} shows two user graphs of controversial and non-controversial posts respectively.

More formally, a post $p$ is represented as a graph \(G = (\mathcal{U}, \mathcal{E}, X)\)  where \(\mathcal{U}=\{u_1, u_2, ..., u_n\}\) denotes the user nodes, \(\mathcal{E}=\{(u_i,u_j)\}_{1\leq i,j\leq n} \)  denotes the edges of the graph, and \(X \in \mathbb{R}^{n\times e} \), \( e \) being the feature dimension,  denotes the user node features matrix.
Each node corresponds to a unique user, and an edge between two nodes exists if there is interaction links between the corresponding users.
The computation of the matrix \(X\) is described in section~\ref{sec:node_feature_extraction}.

\subsection{User feature extraction}\label{sec:node_feature_extraction}
In order to bring valuable information to the graph representation, user features are extracted from the posted texts by using advanced NLP techniques.
Recently, different NLP language models pre-trained on a large corpus have been proposed to improve the dynamic text representation, such as BERT \cite{Devlin2018BERT}.

The user features extraction is performed for each user as follows. Each message (post or comment) a user \(u_i \) posts is firstly cleaned (reddit tags and url link removed) and is then
embedded in an \(e\)-dimensional vector by using  a language model BERT~\footnote{more details on section \ref{sec:experiment}.}.
The embedded vectors obtained from the different messages posted by a user \(u_i \) are  aggregated to form the final
user features \( x_{u_i}\) as shown in equation~\ref{featuresaggregation}.

\begin{equation}
    x_{u_i} = \textsc{Aggregation} \bigg( [ x^0_{{u_i}}, x^1_{{u_i}}, ..., x^m_{{u_i}}] \bigg)
    \label{featuresaggregation}
\end{equation}
where $x_{u_i}^j \in \mathbb{R}^{e}$ is the individual $e$-dimensional embedded vector computed from the $j^{th}$ message of user \(u_i\), and $m$ is the number of messages a user $u_i$ posted.
In this paper, the aggregation of the embedded text vectors is performed via the Max-pooling function, but any other aggregation function can be used. Features of each user $u_i$ is stacked on a matrix \(X \in \mathbb{R}^{n\times e} \).

The user graph \(G = (\mathcal{U}, \mathcal{E},  X) \) is now fully represented and includes node textual features. It will also be referenced by \((A,X)\) where \(A\) represents its adjacency matrix.

\subsection{Graph embedding}\label{sec:graph_embedding}

The graph embedding stage aims to encode the whole user graph in a low-dimensional vector. This latter will fed
the graph classification stage to predict if a post is controversial or not.
Recently, different \textsc{gnn}-based approaches were proposed to adapt deep learning architectures to the graph structured data \cite{Kipf2017GCN, Velickovic2018GAT}.
The main idea is to consider each graph node as a computation node, and to learn classical neural network primitives that compute node embeddings.

This stage relies on \textsc{gnn} architectures with the objective to exploit both user node features computed in the previous stage and the user graph structure of the Reddit discussion. The output is the embedding of the whole graph denoted by $z_{G}$. Learning individual node embeddings denoted by $z_{u_i}$ is also performed as an intermediate stage.
We propose in this paper two main strategies to embed the whole graph for the controversy detection needs.
These strategies rely on hierarchical representations of graphs, convolutional network, and attention-based graph representation.

\subsubsection{Hierarchical graph representation learning-based strategy.}
This strategy exploit hierarchical structure that may exist in the user graph structure.
Thus, in the whole graph encoding process, graph information are aggregated across the edges iteratively and in a hierarchical way.
 We rely on the \textsc{diffpool} \cite{Ying2018Diffpool} approach which encodes the whole graph by stacking several pooling layers.
 Each pooling layer is composed of two distinct \textsc{gnn}: one, called $\textsc{gnn}_{embed}$, learns user nodes embeddings $H$, and the other, called $\textsc{gnn}_{pooling}$, learns an assignment matrix $S$ that indicates which user nodes are assigned to which cluster. The matrix $S$ is used to coarsen the graph.

 As depicted in  Figure~\ref{fig:diffpool_layer}, the functioning of the pooling layer at level (l)  is described as follows:

  \begin{figure*}[!htbp]
	\centering
		\includegraphics[width=1\textwidth]{images/diffpool_layer_paper.png}
	\caption{Diffpool-based Pooling layer architecture. $A^{(l)}$ and $H^{(l)}$  represent the adjacency and feature matrices of the input graph at layer ($l$) respectively. $\textsc{gnn}_{embed}$ and $\textsc{gnn}_{pooling}$ are the 2 \textsc{gnn} blocks used to respectively compute node embeddings $H'^{(l)}$ and assignment matrix $S^{(l)}$. The pooling operation block converts the input graph ($A^{(l+1)}$, $H^{(l+1)}$) into a new coarsened graph ($A^{(l+1)}$, $H^{(l+1)}$).}
	\label{fig:diffpool_layer}
\end{figure*}

\begin{enumerate}
    \item \textit{Node embedding generation}. We first apply the $\textsc{gnn}_{embed}^{(l)}$ to the graph obtained at level $(l)$ represented by its adjacency matrix $A^{(l)}$, and its node features matrix $H^{(l)}$. As described in equation~\ref{nodeembeddingequation}, the result is an intermediate node embeddings $H'^{(l)} \in \mathbb{R}^{m \times d'}$, with $m$ number of nodes of the initial graph of the layer, and $d'$ the new dimensional features.
        \begin{equation}
           H'^{(l)} = \textsc{gnn}_{embed}^{(l)} \bigg( A^{(l)}, H^{(l)} \bigg)
           \label{nodeembeddingequation}
        \end{equation}

    \item \textit{Matrix cluster assignment learning}. We then use the $\textsc{gnn}_{pooling}^{(l)}$ to learn a new assignment matrix $S^{(l)}$ to indicate which nodes of the graph at layer $(l)$ will be clustered together to form a new coarser node at layer $(l)$. The matrix assignment is represented by the equation~\ref{matrixassignmentequation}
    \begin{equation}
           S^{(l)} = \textsc{gnn}_{pooling}^{(l)} \bigg( A^{(l)} , H^{(l)} \bigg)
           \label{matrixassignmentequation}
        \end{equation}

    \item \textit{Nodes and features pooling}. We finally aggregate nodes belonging to the same cluster and their features from $H'^{(l)}$ using the assignment matrix $S^{(l)}$ to output a new coarser graph represented by its adjacency matrix $A^{(l+1)}$ and features matrix $H^{(l+1)}$.
    This pooling operation is done as follows:

        \begin{equation}
           A^{(l+1)} = S^{(l)^{T}} A^{(l)} S^{(l)}
           \label{adjacencymatrix}
        \end{equation}
        \begin{equation}
           H^{(l+1)} = S^{(l)^{T}} H'^{(l)}
           \label{nodefeaturematrix}
        \end{equation}
\end{enumerate}
We can notice that the number of nodes is decreasing at each new layer (l).
 At the first layer (\(l\)=0), $A^0$ and $H^0$   correspond to the   adjacency matrix  \(A \) and the feature matrix \(X \)  of the initial user graph respectively.

The last layer L is a single cluster node, and represents the final vector embedding  $z_{G}$ of the whole graph.

In our work, only one kind of \textsc{gnn} is used: Graph Convolutional Networks (\textsc{gcn}) \cite{Kipf2017GCN}.

\subsubsection{Attention mechanism-based user pooling strategy.}  This strategy is based on attention-based node embedding and allows each user node to judge which user neighbors are more important than the others during the node embedding process, according to the structure and features of the graph. Once node embeddings are generated, they are then aggregated to produce the node embedding of the whole graph.
The attention mechanism (\textsc{gat}) with cardinality preservation~\cite{Zhang2020GATGC} is used
to differentiate user neighbors by assigning them different scores.
This attention mechanism is similar to the Transformers block used in BERT \cite{Devlin2018BERT} for language modelling.

Let's \(\tilde{\mathcal{N}}_{(u_{i})}\) be the multi-set of first-order neighbors of node $u_i$, including $u_i$ itself. This second strategy is described as follows:
\begin{enumerate}
     \item \textit{Neighbors attention score}. For each node \(u_i \in \mathcal{U}\), and each user neighbor \(u_j \in \tilde{\mathcal{N}}_{(u_{i})}\), we first compute the attention score \( e_{u_{i}u_{j}} \) by using an attention function \( a \) on transformed features represented by the matrix \( W^{(l)} \) of the current layer (\(l\)) for both nodes, as described in equation \ref{eq:attention_score}:

     \begin{equation}\label{eq:attention_score}
    e^{(l)}_{u_{i}u_{j}} = a \bigg( \textbf{W}^{(l)} h^{(l)}_{u_{i}}, \textbf{W}^{(l)} h^{(l)}_{u_{j}} \bigg)
     \end{equation}

    \item \textit{Attention scores normalization}. We then normalize scores using a softmax function to get a probability distribution of each score.
    \begin{equation}
        \alpha^{(l)}_{u_{i}u_{j}} = softmax(e^{(l)}_{u_{i}u_{j}}) = \frac{\exp(e^{(l)}_{u_{i}u_{j}})}{\sum_{u_{k} \in \tilde{\mathcal{N}}_{(u_{i})}} \exp(e^{(l)}_{u_{i}u_{k}})}
    \end{equation}

    \item \textit{User node embedding}. The normalized scores are then used to compute the new user node representation \( h^{(l+1)}_{u_{i}} \)

    \begin{equation}\label{simple_head}
        h^{(l+1)}_{u_{i}} = \sigma \Bigg( \sum_{u_{j} \in \tilde{\mathcal{N}}_{(u_{i})}} \alpha^{(l)}_{u_{i}u_{j}} \ \textbf{W}^{(l)} h^{(l)}_{u_{j}} \Bigg)
    \end{equation}
    with \( \sigma \) a non-linear activation function, \(h^{(l+1)}_{u_{i}} \in \mathbb{R}^{n \times d} \) with \(d\) feature dimension of the layer.
    Note that the cardinality preservation allows in~\ref{simple_head} to scale the result before the use of the activation function \( \sigma \).

    The final node representation \( h_{u_{i}} \) corresponds to the output of the last layer \( h^{(L)}_{u_{i}} \).

    \item \textit{Graph embedding}. Finally, we compute the final graph embedding \( z_G \) by applying the READOUT function.
    A  simple graph-level pooling function is used: we sum up each node representation at each iteration layer, and then concatenate them as shown in equation~\ref{readout}.
    \begin{equation}
          z_G =  \mathlarger{\mathlarger{\mathlarger{\parallel}}}^{(L)}_{l=0} \bigg( \readout\Big(\{h^{(l)}_{u_{i}} \Big| u_{i} \in U \}\Big) \bigg)
          \label{readout}
    \end{equation}
\end{enumerate}
This attention mechanism presents multiple advantages, in addition to state-of-the-art results in many benchmark graph classification tasks and interpretability. It allows the use of fixed number of parameters,  and therefore does not depend on the graph size. It also presents transductive and inductive capabilities.

\subsection{Graph Classification}

The graph classification stage aims to classify the post represented by its graph embedding as controversial or non-controversial.
To do so, we simply rely on a classic multi-layer perceptron classifier with the vector \( z_G \) as input.
A Softmax activation on the output layer of dimension 2 is used.

\section{Experimental Evaluation}\label{sec:experiment}

\subsection{Dataset}
We evaluated the performance of our approach using a real-world Reddit dataset, in English, released by Hessel and Lee~\cite{Hessel2019Controversy}.
The same dataset is also covered by Zhong et al.~\cite{Zhong2020ControversyGCN}.
The collected data covers a period from 2007 to February 2014.
It contains 6 specific online channels (also called subreddit): \textit{AskMen} (\textsc{am}), \textit{AskWomen} (\textsc{aw}), \textit{Fitness} (\textsc{fn}), \textit{LifeProTips} (\textsc{lt}), \textit{personnalfinance} (\textsc{pf}), \textit{relationships} (\textsc{rs}).
On Reddit, each user can comment on a post (threads) which is related to a specific topic (subreddit).
Each subreddit contains a set of posts. Metadata and a tree-comment structure are associated to each post.
Finally, only posts with a total of at least 30 comments are kept, assuming that less than 30 comments are not enough to build a significant graph.
Each post is automatically labelled controversial or not controversial, depending on various post meta-data~\cite{Hessel2019Controversy}, among them the ratio between up-votes and down-votes~\footnote{Up-vote and down-vote indicate agreement and disagreement on the post.}.
We first separate our data according to the 6 subreddits.
For each subreddit $s$, we create a set of user graphs \( \mathcal{G}_{s} \), one graph per post, each set having at least 1000 posts.
We then define \( \mathcal{G}_{s,train} \) and \( \mathcal{G}_{s,val} \) as our train and validation graph set respectively, all equally balanced between controversial and non-controversial posts. Considering all aspects and few more experiments, we only evaluate our approach on the same validation set.
The accuracy metric is used to compare the performance of our approach to some existing ones as the dataset is equally balanced.
We separately train the NLP model for user texts representation learning and the \textsc{gnn} for information structure learning.

\subsection{Baseline}
We compared our approach with the following representative
works on controversy detection using the same Reddit dataset.
Note that those methods perform a k-fold to evaluate their performance, using average accuracy as their metric.
\begin{itemize}
\item (POST \textsc{(Text+Time)})~\cite{Hessel2019Controversy}. It  only focuses on the posts content. It uses language modelling based on BERT~\cite{Devlin2018BERT} and extra-features based on the post timestamp of the post.
\item ({\textsc{C-\{Text\_Rate\_Tree\} + Post}})~\cite{Hessel2019Controversy}. It is based on a simple binary classifier.
Textual embeddings of a post are combined with structural features of the comment-tree (average representation of text comments, depth of the tree, etc.) of the post and are used as input of the classifier. We compare post with comment during the first hour and the first three hours.
\item (\textsc{DTPC-GCN})~\cite{Zhong2020ControversyGCN}. It is based on a Disentangled Topic-Post-Comment Graph Convolutional Network. Controversial posts are identified by using \textsc{gcn} model and by learning features depending on the respective subreddit post.
\end{itemize}

\subsection{First experiment: Controversy detection based on structural information}

We implemented our \textsc{gnn}-based controversy detection approach in Pytorch.
The hierarchical graph representation learning is based on \textsc{diffpool}~\cite{Ying2018Diffpool} and \textsc{gcn}~\cite{Kipf2017GCN}. We refer to it as \textbf{HRL-GCN} (Hierarchical Representation Learning based on \textsc{gcn}).
We also test our strategy by using one and two pooling layers, respectively. For each pooling layer, a 3-layer \textsc{gcn} is used.
We rely on the same loss and optimizer functions used in \textsc{diffpool} experimentation~\cite{Ying2018Diffpool}.
The attention-based node learning is based on \textsc{gat}~\cite{Velickovic2018GAT}, using \textsc{gat-gc} method, with the same hyperparameters used in~\cite{Zhang2020GATGC} . We refer to it as \textbf{ARL-GAT}. We also test this strategy with two different node aggregators to compute the whole graph embedding, namely \textsc{mean} and \textsc{sum} pooling functions.
Both \textsc{gnn}-based strategies are trained with a learning rate at 0.01, a batch size at 32 during 100 epochs. Table \ref{tab:stats_dataset} shows statistics on the different datasets.

\begin{table}
    \centering
    \caption{Statistics on the 6 real-world balanced Reddit datasets.}  \label{tab:stats_dataset}

    \begin{tabular}{|l|l|l|l|l|l|l|}
    \hline
     & \ \textsc{am} & \ \textsc{aw} & \ \textsc{fn} & \ \textsc{ls} & \ \textsc{pf} & \ \textsc{rs} \\
    \hline
    Number of posts & \ 3305 \ & \ 2969 \ & \ 3934 \ & \ 1573 \ & \ 1004 \ & \ 2248 \ \\
    \hline
    Average number of user by post \ & \ 72  & \ 67 & \ 76 & \ 79 & \ 47 & \ 48 \\
    \hline
    Average number of comment by post \ & \ 144  & \ 141 & \ 159 & \ 132 & \ 95 & \ 98 \\
    \hline
    Average number of words by comment \ & \ 41  & \ 42 & \ 34 & \ 28 & \ 52 & \ 61 \\
    \hline
    \textcolor{black}{Ratio of comments with tokens $\geq$ 256} & \ 2.68  & \ 2.64 & \ 1.61 & \ 1.03 & \ 4.1 & \ 6.17 \\
    \hline
    \end{tabular}
\end{table}

First experiments are performed without text representation to underline the importance of structural interaction between users in controversial discussion.
Table~\ref{tab:results_gnn} reports the accuracy results
where the first four lines correspond to the baseline and the last four lines correspond to our experiments results.

\begin{table}
    \centering
    \caption{Performance comparison of our \textsc{gnn}-based controversy detection with baseline. Performance is evaluated using accuracy of the validation set.}  \label{tab:results_gnn}

    \begin{tabular}{|l|l|l|l|l|l|l|}
    \hline
     & \ \textsc{am} & \ \textsc{aw} & \ \textsc{fn} & \ \textsc{ls} & \ \textsc{pf} & \ \textsc{rs} \\
    \hline
    POST \textsc{(Text+Time)} & \ 68.1 \ & \ 65.4 \ & \ 65.5 \ & \ 66.2 \ & \ 66.5 \ & \ 69.3 \ \\
    \hline
    \textsc{DTPC-GCN} & \multicolumn{6}{c|}{67.6} \\
    \cline{2-7}
    POST + \textsc{C-\{Text\_Rate\_Tree\}} $<$ 1 hour \ & \ 71.1  & \ 70 & \ 68.1 & \ 67.9 & \ 66.1 & \ 65.5 \\
    POST + \textsc{C-\{Text\_Rate\_Tree\}} $<$ 3 hours \ & \ \textbf{74.3} & \ 72.3 & \ 70.5 & \ \textbf{71.8} & \ \textbf{69.3} & \ \textbf{67.8} \\
    \hline
    \textsc{ARL-GAT} (\textsc{mean}-aggr) & \ 65.7 & \ 69.2  & \ \underline{\textbf{72.4}} & \ 58.4 & \ 53.7 & \ 62.9 \\
    \textsc{ARL-GAT} (\textsc{sum}-aggr) & \ 67.5 & \ 71 & \ 72.2 & \ 67 & \ 63.7 & \ 51.8 \\
    \textsc{HRL-GCN} (pool=2) & \ 69 & \ 72.2 & \ 71.7 & \ \underline{68.3} & \ 65.7 & \ 63.6 \\
    \textsc{HRL-GCN} (pool=1) & \ \underline{69.6} & \ \underline{\textbf{74.6}} & \ 72.2 & \ 67.9 & \ \underline{68.2} & \ \underline{66.7} \\
    \hline
    \end{tabular}
\end{table}

As shown in Table \ref{tab:results_gnn}, our hierarchical approach (\textsc{HRL-GCN}) gets the best results among our experiments, with a weighted average accuracy at $70.6$ using only one pooling layer. Our Attention-based approach \textsc{ARL-GAT} reaches $66.8$ and $66.2$ with the \textsc{sum} and \textsc{mean} aggregator, respectively.
\textsc{HRL-GCN} beats the \textsc{DPTC-GCN}~\cite{Zhong2020ControversyGCN} method and the hybrid method proposed by Hessel and Lee~\cite{Hessel2019Controversy} with comments of the first hour for almost every dataset.
Our proposed method (\textsc{HRL-GCN}, pool=1) gets around state-of-the-art results on several datasets, even going up to $74.6$ accuracy in the \textsc{aw} dataset, beating results in \textsc{C-\{Text\_Rate\_Tree\} + Post} with comments of more than the first three hours.
As the \textsc{am} dataset is the biggest dataset, it could mean that our approach generalizes better when data are abundant, and less when data are sparce.
As explained in~\cite{Hessel2019Controversy}, not enough comments are available for each dataset of the baseline. Indeed, when the subreddit \textit{AskMen} (\textsc{am}) has in average 10 comments after 45min, \textit{Relationships} (\textsc{rs}) does not even have those after 3 hours.
With more available comments, we might have a better embedding representation of our graph, which could lead to better performance.

Table \ref{tab:results_gnn} shows that our attention-based approach \textsc{ARL-GAT}, combined with the \textsc{mean}-aggregator, performs well in the three first datasets, beating our best baseline method on \textsc{fn}, with an accuracy of $72.4$ on the validation set. On the other hand, it underperforms on the other three, falling to $53.7$ on \textsc{pf}.
\textsc{pf} and \textsc{rs} already have low results on our baseline, which means that the data is difficult to understand. This could also be explained by the fact that those 3 subreddits have the least average number of comments (as shown in Table \ref{tab:stats_dataset}), and therefore each user node has less neighbors. Attention scores are in fact less useful in these cases. In general, higher average degree of nodes
could lead to better performance.

\subsection{Second experiment: textual content and structural information based on controversy detection}

We conducted a second experiment to study the impact of adding textual node features to our \textsc{gnn}-based architecture.
Instead of considering all options of our \textsc{gnn}-based architecture shown in Table~\ref{tab:results_gnn}, we only considered our hierarchical representations strategy \textsc{HRL-GCN}, with one pooling layer, as it realises the best accuracy scores.

Text features of comments and posts are extracted using different language model based on BERT, and are aggregated by user to be used as the initial features of our user nodes.

We use different models to extract those features:
\begin{itemize}
    \item \textsc{PT} model. It only uses the pre-trained features to get the message representation. The last layer (768 dimensions) is outputted as our text embeddings.

   \item \textsc{FT\_itself} model.
    We fine-tune \cite{Sun2020BERTFineTuning} a BERT model using comments and posts of our train set \( \mathcal{G}_{s,train} \), with an extra-layer of 64 neurons on top, in addition to the classifier layer. We label each comment with the controversy label of its respective post.
    Note that each subreddit is fine-tuned separately as we suppose that different communities express themselves differently, and texts can be interpreted differently.

    \item \textsc{FT\_sentiment} model. We fine-tune a BERT model using sentiment analysis with another Reddit dataset of comments (hosted on kaggle.com),

    labeled as negative, positive or neutral. Indeed, we suppose here that sentiments can outline users' behavior on controversial posts.

\end{itemize}
In all cases, we use the 'base-bert-uncased' version (with its corresponding tokenizer), with 12 transformer layers and 110 millions parameters. For time and memory performance, we only use 256 tokens max per text (instead of 512, as Table \ref{tab:stats_dataset} shows that in average, less than 3\% of the messages are represented by more than 256 tokens). For fine-tuning models, we use the same hyperparameters used in \cite{Sun2020BERTFineTuning}.
Table~\ref{tab:results_gnn_text} shows the new accuracy scores obtained by incorporating text features in our \textsc{HRL-GCN} strategy.

\begin{table}
    \centering
    \caption{Performance of our best \textsc{gnn} approach enriched with different user text embeddings as initial node features.}\label{tab:results_gnn_text}
    \begin{tabular}{|l|l|l|l|l|l|l|}
    \hline
     & \ \textsc{am} & \ \textsc{aw} & \ \textsc{fn} & \ \textsc{ls} & \ \textsc{pf} & \ \textsc{rs} \\
    \hline
    \ \textsc{HRL-GCN} (pool=1) \ \ \ \ & \ 69.6 \ & \ \underline{\textbf{74.6}} \ & \ \underline{72.2} \ & \ 67.9 \ & \ 68.2 \ & \ \underline{66.7} \ \\
    \hline
    \ \ \ \ + \textsc{FT\_sentiment} \ & \ 69.1 \ & \ 72.9 \ & \ 70.5 \ & \ \underline{68.6} \ & \ 66.7 \ & \ 64 \ \\
    \ \ \ \ + \textsc{FT\_itself} \ & \ 67.3 \ & \ 73.9 \ & \ 71.8 \ & \ 68.3 \ & \ \underline{70.6} \ & \ 63.8 \ \\
    \ \ \ \ + \textsc{PT} \ & \ \underline{70.8} \ & \ 73.7 \ & \ 71 \ & \ 65.4 \ & \ \underline{70.6} \ & \ 64.7 \ \\
    \hline
    \end{tabular}
\end{table}

For three of the six datasets, adding textual features improve controversy detection results.
Our \textsc{HRL-GCN} strategy combined with the pre-trained (\textsc{PT}) BERT features gets better results when using \textsc{am} and \textsc{pf} datasets, with  $70.8$ and $70.6$ accuracy result respectively.
Adding sentiment features from \textsc{FT\_sentiment} allows us to increase accuracy from $67.9$ to $68.6$  on \textsc{ls}.
Even if the content has interesting features for controversy detection, it remains brittle and community specific, which means that textual features can be more impactful in some subreddits than others. For instance, sentiments about controversial topic can be more meaningful in subreddit \textit{LifeProTips} (\textsc{lt}) than in more personal subreddits, like \textit{Relationships} (\textsc{rs}).
The complexity of the data and the fact that datasets might be too small compared to the number of features (which goes up to $768$ when using \textsc{PT} model) could also explain why our \textsc{gnn}-based models overfit on some datasets, and therefore does not improve accuracy results.

  \begin{figure*}[!htbp]
	\centering
		\includegraphics[width=0.55\textwidth]{images/accuracy_by_100_one_plot.png}

	\caption{Impact of comments availability on controversy detection performance.}
	\label{fig:plot_accuracy_timebased}
\end{figure*}

Figure \ref{fig:plot_accuracy_timebased} shows the importance of comments availability.
It reports accuracy results evolution over time (minutes) of three datasets when using our best \textsc{HRL-GCN} strategy combined with text features from PT.
It clearly shows that the more available comments we have, the easier it is to detect controversy.

\section{Conclusion}

We presented an automatic controversy detection method on social media, based on \textsc{gnn} techniques.
We considered this detection as a classification task and first exploited the
structural information that characterizes user interactions by
defining two strategies of graph embedding.
The first strategy exploits hierarchical structure that may exist in the user graph.
The second strategy allows each user to select its neighbors in the embedding nodes process.
We also improved the graph embedding by incorporating textual content features
computed from BERT model.
Experimental evaluation shows promising results, even beating our baseline in several datasets~\footnote{\textcolor{black}{This work was supported by grants from Janssen Horizon endowment fund}}. However, our current Reddit dataset shows its limits, as a post has usually few comments, which prevents our \textsc{gnn}-based model from getting a better graph representation for controversy detection. The use of a different platform, such as Twitter, which provides more data per topics, \textcolor{black}{or Wikipedia}, could be an interesting lead to follow.
In terms of future work, we would like to examine the appropriateness
of other \textsc{gnn} techniques for controversy detection. For instance, it could be interesting to study the impact, in terms of
performance improvements, of using \textsc{gnn} architectures that take into account nodes properties,
\textcolor{black}{mixing then structural, textual and user information.}
\textcolor{black}{compare results from different social media at the same time could also help to have a better understanding of the subject covered. Quantifying controversy using our approach is also an interesting perspective.}

\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


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Halifax Canada (2015)
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Halifax is part of Canada's federal state of Nova Scotia. The peninsula has a population of 333,000 and it is the country's oldest British settlement, yet has managed to retain all the charm and tranquility of a small town.
After Sydney, Halifax has the second largest harbor in the world and is one of the region's few harbors that is free from ice throughout the year.
Lobsters, oysters, mussels and fish soup are but a few of the menu items that appear in the seafood restaurants of the Atlantic District.
The Maritime Museum covers the history of the fishing industry on Canada's Atlantic coastline and also the history of navigation. Boats and small models illustrate the development of ship-building and include great seafarers and many remarkable exhibits.
In 1917, two ships collided in Halifax Harbor. This led to an explosion that destroyed half the city when 2,000 people perished and 9,000 were injured. In April 1912, the loss of the supposedly unsinkable passenger ship, the Titanic, was caused to its collision with an iceberg and it was from Halifax that the rescue ships embarked.
The famous train, The Ocean, embarks from close to the harbor and its impressive stainless steel carriages date back to the 1950s.
Halifax is an historic city set in a country with a vast and amazing landscape, a flamboyant metropolis that unites the past with the present day.
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text:
A lob wedge, also known as a lofted wedge or an L-Wedge, is a wedge used in the sport of golf, known for being one of the shortest-hitting clubs and providing the most loft on a shot. Lob wedges are used to produce shots with a very high arc, and are most often used for shots over hazards and other obstructions. Due to the high arc of the shot the lob wedge, like the other wedges in the set of irons, produces little roll after landing on the putting green and can even be used to produce backspin if necessary. Lob wedges are one of the newest additions to the modern collection of golf clubs and, along with the sand wedge and gap wedge, were not included prior to 1931.

Design
Lob wedges are part of the iron family of golf clubs and are designed for short, high arc shots. Lob wedges and ultra lob wedges are designed with the shortest shafts and the highest loft of all golf clubs. Regular lob wedges may come in a variety of lofts, usually starting around 56 degrees and approaching 60. While those above 60 degrees are rare, often up to 64 degrees, they do exist and are referred to as "x-wedges", extreme lob wedges, or ultra lob wedges. Other differences between the lob wedge and the rest of the wedge family include the less pronounced flange on the sole allowing the club to slide under the ball more easily and less degrees of bounce (often between 0–10 degrees).  The low bounce was designed out of the necessity of professionals who found that the higher bounce sand wedge was making it difficult to produce the spin they desired, especially out of tight lies

Ultra lob wedge
An ultra lob wedge is used to hit a shot with higher and shorter trajectory than a standard lob wedge. This club is labeled "UL" and has a loft of about 64° and has the highest loft of any golf club. It is used for specialized, extremely high-angle shots such as from the "lip" of a bunker. This wedge is generally made by speciality companies and some argue that their purpose is redundant, as other wedges can be "opened" for extra loft in situations calling for such a high launch angle. Furthermore, to get consistent results, this club requires skill – in particular, distance judgment is always key with such a lofted club. It can be noted that different lengths of swing will often yield a similar distance but with varying trajectories and thus roll (on the green).

Use
Lob wedges can be used for a variety of shots including pitch and runs and pitching over an obstacle, particularly shots requiring a very high arc, a large amount of backspin or both. Pitching over an obstacle is used in situations where a hazard of some sort, usually a water hazard, bunker or tree, is located in the line of the shot between the ball's current location and the target (often on the putting green). By utilizing a lob wedge for these short shots, the ball is carried much higher in the air than with a standard iron causing significantly less roll on the landing surface.  This is most often important for short shots into difficult pin placements. Before the invention of the lob wedge, or to create a shot similar to the lob wedge, the golfer was required to perform the shot with a sand wedge or pitching wedge and to modify the shot by cutting across the ball making a glancing blow, a much riskier shot.

History
Traditional sets of golf irons did not always include the lob wedge.  Before 1931, golfers used a single wedge, known as a "jigger", similar to the modern day pitching wedge. After 1931, additional wedges entered the golf bag starting with the sand wedge.  The lob wedge was first envisioned by Dave Pelz, a former physicist for NASA, who recognized the need for higher loft wedges (at the time 60 degrees) due to the increasing complexity of the putting greens being designed at the time.  This concept was taken up by the professional golfer Tom Kite, who began using the lob wedge professionally on tour spurring other professionals to follow his lead. The mainstream lob wedge was invented by Karsten Solheim, the founder of the PING line of golf products, possibly after experimenting by gluing a potato chip to a straw.  Solheim was also responsible for naming the club the "L Wedge" or the "Lob Wedge", as it is currently known today.

References

Golf clubs
Golf terminology

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text:
California Governor Gavin Newsom
Make California Great Again! Vote!
In 2016, Steven Choi ran for Assembly with a picture of himself and Donald Trump, saying that he and Trump would "Make California Great Again."
Here's how that has worked out:
· More than 220,000 Americans dead of COVID-19 and infections continue to surge, hitting record high numbers eight months into the pandemic. More than 70,000 Californians infected with the coronavirus, but Trump and Choi deny science and have no plan.
· Nearly 17,000 Californians dead so far from COVID-19, but Trump and Choi still have no plan.
· Thousands of California businesses closed, millions of jobs lost, and our economy in shambles, but Trump and Choi still have no plan.
· More than 4.1 million acres burned, 31 dead, and 9,200 structures destroyed in catastrophic, climate-change caused/exacerbated wildfires, but Trump and Choi deny climate change.
· Trump and Choi blame California, not climate change, for the increase in wildfires. Trump tells us to "rake the leaves." Choi is silent.
· Trump denies emergency federal aid to California for wildfire victims and first responders. Choi is silent.
· Trumps increases offshore oil drilling on California's coast, threatening major environmental damage to our ocean and beaches. Trump and Choi take $$$$ from major polluters.
· Trump calls COVID-19 the "Kung flu" and "China virus." As a result, violence and hate is directed toward Asian Americans. Choi is silent.
· Trump attacks Mexican Americans and Muslim Americans, calls for building a wall with Mexico and bans entry to U.S. from Muslim countries. Choi agrees with Trump's wall and his Muslim ban, calls Muslims "unfit for office."
Trump and Choi have made California sick, not great.
Now it's our turn to make California great again by voting them out.
Unlike Trump and Choi, I'll never put our communities at risk.
Posted in Uncategorized | Tagged 2020 Election, 68th AD, 68th Assembly District, AAPI, AD68, Anti-Asia bias, anti-Asian attacks, anti-Asian bias, anti-Asian discrimination, anti-Asian prejudice, Asian American, Asian Americans, Asian Americans Rising, Asian Pacific Islander Legislative Caucus, Biden Harris, Biden Harris 2020, bigotry, bullying, California, California 68 AD, California 68th AD, California 68th Assembly District, California Department of Public Health, California election, California Governor Gavin Newsom, California Governor Newsom, California primary election, California primary election 2020, CDC, Centers for Disease Control and Prevention, Coronavirus, Coronavirus COVID-19, Coronavirus Disease, Councilmember メリッサ・フォックス, Councilmember Melissa ਫਾਕਸ, Councilmember میلیسا فاکس, COVID-19, David Chiu, Democratic candidates, Donald J. Trump, election 2020, election 2020 key dates, Extremist Steven Choi, 멜리사 폭스, Find ballot drop box, find your vote center, Fox 68th AD, Fox 68th Assembly District, Fox California 68 AD, Fox California 68th AD, Fox California 68th Assembly District, Fox4AD68, Gavin Newsom, Governor Gavin Newsom, Governor Newsom, http://votemelissafox.com, https://www.extremiststevenchoi.com/, https://www.extremiststevenchoi.com/pandemic-response, https://www.extremiststevenchoi.com/silence-on-trump-bigotry, Irvine, Irvine California, Irvine Chinese, Irvine culture, Irvine diversity, Irvine Filipino, Irvine Indian, Irvine Japanese, Irvine Korean, Irvine multicultural, Irvine Police, Irvine Thai, Irvine Vietnamese, Japanese, Josh Newman, Kamala Harris, Katie Nguyen Kalvoda, KHB, Khmer, Konseyi Üyesi Melissa Fox, Korean, mail-in ballots, Melissa Fox, Melissa Fox AD68, Melissa Fox blog, Melissa Fox for 68 Assembly District, Melissa Fox for 68th AD, Melissa Fox for 68th Assembly District, Melissa Fox for Irvine, Melissa Fox for Irvine City Council, Melissa Fox for State Assembly, Melissa Fox Irvine, melissafoxblog, melissafoxblog.com, melissajoifox, Mike Hamel, Miyembro ng Konseho Melissa Fox, Nghị viên Melissa Fox, November 3 election, OC Registrar of Voters, Orange County Registrar of Voters, Խորհուրդի Melissa Fox, President Trump, public health, racism, register to vote, Senator Kamala Harris, State of California, Stop AAPI Hate, Trump, Vice President Joe Biden, Vote Centers, votemelissafox.com, World Health Organization, xenophobia, 市議員 梅利莎福克斯, 市议员 梅利莎福克斯
Asian Americans Rising Honors Joe Biden, Kamala Harris, Katie Porter, Melissa Fox and Other Political Leaders for Standing with the Asian American Community Against Xenophobia and Racism
This week, Asian Americans Rising, a non-profit group "committed to increasing Asian American representation in politics," issued a statement thanking Orange County political leaders who "stood with us to denounce xenophobia and racism" when the Asian American community was under attack.
I am deeply honored to be included among these courageous political leaders.
Asian Americans Rising president Katie Nguyen Kalvoda explained:
"Over 2,000 hate incidents were directed at Asian Americans this year as a result of Trump's hateful words calling the coronavirus the "kung flu" "Chinese virus". Women, children, grandmothers of all Asian descent were attacked, stabbed, set on fire all across this country. I would have never imagined the day that I would bear witness to that. Me, my kids, our loved ones are viewed as the 'yellow plague.' This is why I appreciate so much the folks who have spoken out, denounced racism and shown us love."
Asian Americans in California have reported thousands of incidents of discrimination and harassment in since the coronavirus outbreak, including assault and civil rights violations.
Anti-Asian American attacks and harassment have been stoked by President Trump's repeated use of the term "Kung Flu" in recent rallies and comments on Twitter scapegoating China for the Trump administration's catastrophic failure to control the pandemic. As California Assemblymember David Chiu, Chair of the Asian Pacific Islander Legislative Caucus, told the Los Angeles Times, "There's not just a pandemic of health — there's a pandemic of hate."
The Washington Post recently reported that "when Trump get coronavirus, Chinese Americans pay a price." On Twitter, in the three days after Trump announced that had tested positive for the virus, the civil rights group the Anti-Defamation League found an 85 percent spike in hostility against Asians: "The announcement [of Trump's diagnosis] sparked thousands of online conversations blaming China for trying to purposefully infect the president."
I am appalled by these acts of bigotry and by President Trump's continued stoking of anti-Chinese and anti-Asian hate.
I am also appalled by the silence of Republican leaders in the face of Trump's anti-Asian rhetoric.
Sadly, even Republican leaders who are themselves Asian, including Assemblyman Steven Choi, have refused to protest Trump's use of the racist and anti-Asian phrase "Kung Flu" in talking about COVID-19 and have silent about the significant increase in racist attacks targeting Asians and Asian Americans in the wake of the COVID-19 pandemic.
I first raised the issue of the COVID-19 outbreak and incidents of discrimination, harassment, and bullying of people thought to be Chinese at the Irvine City Council meeting on more than a month ago, on March 10. I stated that we needed to do more to educate the public about how racism and xenophobia will hurt us in this crisis, and that we are all in this together.
I continue to be concerned, especially as reports increased of a surge in racially charged attacks unfairly directing blame for the pandemic on Asians and Asian Americans, while President Trump continues to insist on using the phrase "Chinese virus" or "Kong Flu" when speaking of COVID-19.
All who have witnessed or experienced anti-Asian attacks are encouraged to file a report HERE.
Reports may be made in English, Chinese, Korean, Vietnamese, Hindi, Japanese, Hmong, Tagalog, Khmer, Thai and Punjabi.
If you have experienced anti-Asian bullying, harassment, hate speech, or violence in Irvine, please also contact the Irvine Police Department at 949-724-7000. In an emergency, call 911. Neither the Irvine Police Department nor the Irvine City Council will tolerate any such anti-Asian attacks or discrimination in Irvine.
Please also let me know at melissafox@cityofirvine.org.
Again, I call on all my colleagues in elected office in Orange County, both Democratic and Republican, to join me in loudly and unequivocally condemning these acts of hatred, as well as President Trump's continued stoking of anti-Chinese and anti-Asian hatred and bigotry by using the terms "Chinese virus" and "Kung Flu" in reference to COVID-19.
Pictured above: Congresswoman Judy Chu, Congresswoman Katie Porter, Councilmember Andrew Rodriguez, Scott Reinhart, Congressman Alan Lowenthal, Congressman Gil Cisneros, Josh Newman, California State Controller Betty T. Yee, Councilmember Diedre Thu-Ha Nguyen, Congressman Harley Rouda, City Councilmember Melissa Fox, Assemblywoman Cottie Petrie-Norris, Congressman Lou Correa, Congressman Mike Levin, Senator Kamala Harris, and Vice President Joe Biden.
Posted in Uncategorized | Tagged 68th AD, 68th Assembly District, AAPI, Anti-Asia bias, anti-Asian attacks, anti-Asian bias, anti-Asian discrimination, anti-Asian prejudice, Antonio Guterres, Asian American, Asian Americans, Asian Americans Rising, Asian Pacific Islander Legislative Caucus, Assemblymember David Chiu, Assemblymember David Chiu San Francisco, Assemblywoman Cottie Petrie-Norris, bigotry, bullying, California, California 68 AD, California 68th AD, California 68th Assembly District, California Department of Public Health, California Governor Gavin Newsom, California Governor Newsom, California OSHA, California State Controller Betty T. Yee, CDC, Centers for Disease Control and Prevention, Chief Mike Hamel, Chinese, City of Irvine, Congressman Gil Cisneros, Congressman Harley Rouda, Congressman Josh Lowenthal, Congressman Lou Correa, Congressman Mike Levin, Congresswoman Judy Chu, Congresswoman Katie Porter, Coronavirus, Coronavirus COVID-19, Coronavirus Disease, Councilmember メリッサ・フォックス, Councilmember Diedre Thu-Ha Nguyen, Councilmember Melissa ਫਾਕਸ, Councilmember میلیسا فاکس, COVID-19, David Chiu, Donald J. Trump, Extremist Steven Choi, 멜리사 폭스, Fox 68th AD, Fox 68th Assembly District, Fox California 68 AD, Fox California 68th AD, Fox California 68th Assembly District, Gavin Newsom, Global Village Festival, Governor Gavin Newsom, Governor Newsom, Hindi, Hmong, https://www.extremiststevenchoi.com/silence-on-trump-bigotry, Irvine, Irvine California, Irvine Cambodian, Irvine Chinese, Irvine Chinese School, Irvine culture, Irvine diversity, Irvine Filipino, Irvine Indian, Irvine Japanese, Irvine Korean, Irvine multicultural, Irvine Police, Irvine Thai, Irvine Vietnamese, Japanese, Josh Newman, Katie Nguyen Kalvoda, KHB, Khmer, Konseyi Üyesi Melissa Fox, Korean, Melissa Fox, Melissa Fox blog, Melissa Fox for 68 Assembly District, Melissa Fox for 68th AD, Melissa Fox for 68th Assembly District, Melissa Fox for Irvine, Melissa Fox for Irvine City Council, Melissa Fox for State Assembly, Melissa Fox Irvine, melissafoxblog, melissafoxblog.com, melissajoifox, Mike Hamel, Miyembro ng Konseho Melissa Fox, Nghị viên Melissa Fox, OSHA, Խորհուրդի Melissa Fox, Paid Family Leave, President Trump, public health, Racial Bias Strike Team, racism, San Francisco State University, San Francisco State University Asian American Studies Department, San Francisco State University's Asian Am, Scott Reinhart Councilmember Andrew Rodriguez, Segio Contra City Councilmember Melissa Fox, Senator Kamala Harris, State of California, Stop AAPI Hate, Tagalog, Thai and Punjabi., Trump, United Nations Secretary-General Antonio Guterres, Vice President Joe Biden, Vietnamese, WHO, World Health Organization, xenophobia, 市議員 梅利莎福克斯
California Moves to Dramatically Lower Prescription Drug Prices
Last week, California Governor Gavin Newsom signed into law new first-in-the-nation legislation that could dramatically reduce prescription drug prices for all Californians.
The legislaton allows the State of California to create its own drug label, Cal Rx, and to produce and distribute its own line of biosimilars, biosimilar insulins, and generic drugs, with the aim of improving access for consumers and lowering prices.
The California Health and Human Services Agency (CHHS) is now authorized to develop manufacturing partnerships to produce or distribute generic prescription drugs, making essential medications affordable and accessible to more patients. It will also inject much needed competition into near monopoly markets that have driven up prices for consumers and help end sometimes critical drug shortages.
Councilmember Melissa Fox and Senator Richard Pan
Because precription drug prices are one of the largest drivers of rising health care costs, this new legislation will also reduce the overall cost of health care.
As Governor Newsom said in advocating for the new law, "Prescription drug prices are too high. I'm proposing that California become the first state in the nation to establish its own generic drug label. It's time to take the power out of the hands of greedy pharmaceutical companies."
Governor Newsom further noted, "The cost of health care is way too high. Our bill will help inject competition back into the generic drug marketplace – taking pricing power away from big pharmaceutical companies and returning it to consumers. California is using our market power and our moral power to demand fairer prices for prescription drugs. I am proud to sign this legislation affirming our ground-breaking leadership in breaking down market barriers to affordable prescription drugs."
As the legislation's principle author, Dr. (and State Senator) Richard Pan, pointed out, "Prescription drugs don't work if people cannot afford to take them. We need to ensure that Californians will be able to have access to a reliable supply of affordable generic medications. The state can play a pivotal role in bringing prices down through this authority to negotiate a steady supply for all purchasers and an increase of competition in the drug markets," He added that the new legislation to open up access to affordable drugs for millions of Californians "is more important than ever, as the COVID-19 crisis brought to light glaring gaps in supplies of essential, lifesaving drugs, and medical equipment and supplies."
I strongly support this new and innovative approach to lowering precsription drug prices, making critical presciption medicine and health care more available as well as more affordable.
In contrast, my opponent in Assembly District 68, Steven Choi, refused to support this important legislation and did not even vote on this bill.
His campaign has received tens of thousands of dollars from the pharmaceutical and medical industries and their political action committies. In addition, according to his legally required Statements of Economic Interests filed with the California Fair Political Practices Commission, Choi holds substantial investments in numerous pharmaceutical companies, including Pfizer, Sanofi, Johnson &Johnson, GlaxoSmith-Kline, Celgene, and Novo Nordisk, so that he personally profits from high presciption drug prices.
Unlike Steven Choi, I'll be part of the solution to the high cost of prescription drugs, not part of — or profit from — the problem.
Posted in Uncategorized | Tagged 68th AD, 68th Assembly District, affordable health care, affordable healthcare, Anaheim Hills, Assembly District 68 -- Lake Forest, California 68 AD, California 68th AD, California 68th Assembly District, California Governor Gavin Newsom, California Health and Human Services Agency, California Health and Human Services Agency (CHHS), CalRx, CHHS, City of Anaheim, City of Irvine, Coronavirus, Coronavirus COVID-19, cost of health care, Councilmember メリッサ・フォックス, Councilmember Melissa ਫਾਕਸ, Councilmember میلیسا فاکس, COVID-19, COVID-19 Pandemic, COVID19 Orange County, Dr. Richard Pan, drug addiction, Extremist Steven Choi, 멜리사 폭스, Fox 68th AD, Fox 68th Assembly District, Fox California 68 AD, Fox California 68th AD, Fox California 68th Assembly District, Gavin Newsom, Governor Gavin Newsom, Governor Newsom, health care, healthcare, https://www.extremiststevenchoi.com/, https://www.extremiststevenchoi.com/pandemic-response, Irvine, Irvine California, irvine City Council Member, Irvine City Councilmember Melissa Fox, Irvine Melissa Fox, Konseyi Üyesi Melissa Fox, lowering prescription drug costs, melisafoxblog.com, Melissa Fox, Melissa Fox attorney, Melissa Fox blog, Melissa Fox for 68 Assembly District, Melissa Fox for 68th AD, Melissa Fox for 68th Assembly District, Melissa Fox for Irvine, Melissa Fox for State Assembly, Melissa Fox Irvine, Melissa Fox law, Melissa Fox youtube, Melissa Fox youtube channel, melissafoxblog, melissafoxblog.com, melissafoxlaw.com, melissajoifox, Michelle Park Steel, Miyembro ng Konseho Melissa Fox, Nghị viên Melissa Fox, Orange, Orange County COVID-19, Խորհուրդի Melissa Fox, prescription drug costs, prescription drug prices, Richard Pan, SARS-CoV-2 virus, Senator Richard Pan, Steven Choi campaign funding, Steven Choi drug companies, Steven Choi investments, Tustin, Villa Park, votemelissafox, votemelissafox.com, 姜石熙, 尔湾市议员 Melissa Fox, 市議員 梅利莎福克斯, 市議員梅利莎福克斯, 市议员 梅利莎福克斯, 市议员梅利莎福克斯, 一員メリッサ・フォックス, 中国美国, 中國美國, العضو في المجلس ميليسا فوكس
California Needs a Racial Bias Strike Team Against Anti-Asian COVID-19 Racism
Asian Americans in California have self-reported 832 incidents of discrimination and harassment in the last three months, including 81 incidents of assault and 64 potential civil rights violations, according to Stop AAPI Hate, a reporting center and the leading aggregator of incidents against Asian Americans during the pandemic, founded by the Asian Pacific Policy and Planning Council (A3PCON) and San Francisco State University Asian American Studies Department.
As California Assemblymember David Chiu, Chair of the Asian Pacific Islander Legislative Caucus, told the Los Angeles Times, "There's not just a pandemic of health — there's a pandemic of hate."
Discrimination and harassment of Asian Americans in California has drawn national attention recently after a series of videos in Torrance, California, featured a woman using graphic racist language against Asian Americans. The videos have received millions of views, and reflect just a handful of the incidents reported to Stop AAPI Hate in California. The new report shows that incidents of racism and discrimination are not isolated to any particular area but are a statewide problem — Asian Americans have reported incidents in 34 counties so far. Incidents are reportedly taking place in California in retail stores, in the workplace, and online.
Anti-Asian American harassment has been further stoked by President Trump's repeated use of the term "Kung Flu" in recent rallies and comments on Twitter scapegoating China for the United States' devastating failure to control the coronavirus.
I am appalled by these acts of hatred and by President Trump's continued stoking of anti-Chinese and anti-Asian bigotry.
I continue to be concerned, especially as reports increased of a surge in racially charged attacks unfairly directing blame for the pandemic on Asians and Asian Americans, while President Trump insists on using the phrase "Chinese virus" or "Kong Flu" when speaking of COVID-19.
In May, United Nations Secretary-General Antonio Guterres said that "the pandemic continues to unleash a tsunami of hate and xenophobia, scapegoating and scare-mongering" and urged governments to "act now to strengthen the immunity of our societies against the virus of hate."
In response to these attacks, Stop AAPI Hate has now called on California Governor Gavin Newsom to establish a Racial Bias Strike Team comprised of key state agencies and departments that have jurisdiction over public education, implementing state and federal civil rights laws, overseeing workplace and employment discrimination, providing mental health services to vulnerable communities, and offering support to local Asian American-serving community-based organizations.
As Dr. Russell Jeung, Chair and Professor of Asian American Studies at San Francisco State University, explains, "Without government accountability, we risk COVID-related racism against Asian Americans becoming deeply entrenched, ultimately impacting the lives of millions of people in California and around the country."
I join with Stop AAPI Hate in calling on California Governor Gavin Newsom to establish a Racial Bias Strike Team against anti-Asian COVID-19 racism.
I further call on all my colleagues in elected office in Orange County, both Democratic and Republican, to join me in loudly and unequivocally condemning these acts of hatred, as well as President Trump's continued stoking of anti-Chinese and anti-Asian hatred and bigotry by using the terms "Chinese virus" and "Kung Flu" in reference to COVID-19.
No one, especially not the president, should use racial or racist terms in describing COVID-19.
Sadly, no Orange County Republican elected official has explicitly condemned Trump's racist, anti-Asian "Chinese virus" and "Kung Flu" language regarding COVID-19. Their cowardice and complicity leaves an indelible stain on their party and themselves.
We're in this together. Don't hate, stay safe, and wear a mask!
Posted in Uncategorized | Tagged 68th AD, 68th Assembly District, AAPI, Anti-Asia bias, anti-Asian attacks, anti-Asian bias, anti-Asian discrimination, anti-Asian prejudice, Antonio Guterres, Asian American, Asian Americans, Asian Pacific Islander Legislative Caucus, Assemblymember David Chiu, Assemblymember David Chiu San Francisco, bigotry, bullying, California, California 68 AD, California 68th AD, California 68th Assembly District, California Department of Public Health, California Governor Gavin Newsom, California Governor Newsom, California OSHA, CDC, Centers for Disease Control and Prevention, Chief Mike Hamel, Chinese, City of Irvine, Coronavirus, Coronavirus COVID-19, Coronavirus Disease, Councilmember メリッサ・フォックス, Councilmember Melissa ਫਾਕਸ, Councilmember میلیسا فاکس, COVID-19, David Chiu, Donald J. Trump, 멜리사 폭스, Fox 68th AD, Fox 68th Assembly District, Fox California 68 AD, Fox California 68th AD, Fox California 68th Assembly District, Gavin Newsom, Global Village Festival, Governor Gavin Newsom, Governor Newsom, Hindi, Hmong, Irvine, Irvine California, Irvine Cambodian, Irvine Chinese, Irvine Chinese School, Irvine culture, Irvine diversity, Irvine Filipino, Irvine Indian, Irvine Japanese, Irvine Korean, Irvine multicultural, Irvine Police, Irvine Thai, Irvine Vietnamese, Japanese, KHB, Khmer, Konseyi Üyesi Melissa Fox, Korean, Melissa Fox, Melissa Fox blog, Melissa Fox for 68 Assembly District, Melissa Fox for 68th AD, Melissa Fox for 68th Assembly District, Melissa Fox for Irvine, Melissa Fox for Irvine City Council, Melissa Fox for State Assembly, Melissa Fox Irvine, melissafoxblog, melissafoxblog.com, melissajoifox, Mike Hamel, Miyembro ng Konseho Melissa Fox, Nghị viên Melissa Fox, OSHA, Խորհուրդի Melissa Fox, Paid Family Leave, President Trump, public health, Racial Bias Strike Team, racism, San Francisco State University, San Francisco State University Asian American Studies Department, San Francisco State University's Asian Am, State of California, Stop AAPI Hate, Tagalog, Thai and Punjabi., Trump, United Nations Secretary-General Antonio Guterres, Vietnamese, WHO, World Health Organization, xenophobia, 市議員 梅利莎福克斯, 市议员 梅利莎福克斯


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DOI : 10.1111/j.1348-0421.2006.tb03866.x
S. Chatterjee and K. Chaudhuri, Lipopolysaccharides of Vibrio cholerae, Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, vol.1639, issue.2, pp.65-79, 2003.
DOI : 10.1016/j.bbadis.2003.08.004
S. Villeneuve, H. Souchon, M. Riottot, J. Mazie, P. Lei et al., Crystal structure of an anti-carbohydrate antibody directed against Vibrio cholerae O1 in complex with antigen: Molecular basis for serotype specificity, Proceedings of the National Academy of Sciences, vol.97, issue.15, pp.8433-8438, 2000.
DOI : 10.1073/pnas.060022997
A. Chernyak, S. Kondo, T. Wade, M. Meeks, P. Alzari et al., O1 Ogawa Lipopolysaccharide Bound to a Protein Carrier, The Journal of Infectious Diseases, vol.185, issue.7, pp.950-962, 2002.
DOI : 10.1086/339583
A. Boutonnier, S. Villeneuve, F. Nato, B. Dassy, and J. Fournier, Preparation, Immunogenicity, and Protective Efficacy, in a Murine Model, of a Conjugate Vaccine Composed of the Polysaccharide Moiety of the Lipopolysaccharide of Vibrio cholerae O139 Bound to Tetanus Toxoid, Infection and Immunity, vol.69, issue.5, pp.3488-3493, 2001.
DOI : 10.1128/IAI.69.5.3488-3493.2001
N. Bhuiyan, F. Qadri, A. Faruque, M. Malek, M. Salam et al., Use of Dipsticks for Rapid Diagnosis of Cholera Caused by Vibrio cholerae O1 and O139 from Rectal Swabs, Journal of Clinical Microbiology, vol.41, issue.8, pp.3939-3941, 2003.
DOI : 10.1128/JCM.41.8.3939-3941.2003
F. Nato, A. Boutonnier, M. Rajerison, P. Grosjean, S. Dartevelle et al., One-Step Immunochromatographic Dipstick Tests for Rapid Detection of Vibrio cholerae O1 and O139 in Stool Samples, Clinical and Vaccine Immunology, vol.10, issue.3, pp.476-478, 2003.
DOI : 10.1128/CDLI.10.3.476-478.2003
J. Navaza, : an automated package for molecular replacement, Acta Crystallographica Section A Foundations of Crystallography, vol.50, issue.2, pp.157-163, 1994.
DOI : 10.1107/S0108767393007597
G. Murshudov, A. Vagin, and E. Dodson, Refinement of Macromolecular Structures by the Maximum-Likelihood Method, Acta Crystallographica Section D Biological Crystallography, vol.53, issue.3, pp.240-255, 1997.
P. Emsley and K. Cowtan, : model-building tools for molecular graphics, Acta Crystallographica Section D Biological Crystallography, vol.60, issue.12, pp.2126-2132, 2004.
G. Andre-leroux, D. Tessier, and E. Bonnin, Action pattern of Fusarium moniliforme endopolygalacturonase towards pectin fragments: Comprehension and prediction, Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, vol.1749, issue.1
DOI : 10.1016/j.bbapap.2005.02.008
M. Mayer and B. Meyer, Characterization of Ligand Binding by Saturation Transfer Difference NMR Spectroscopy, Angewandte Chemie International Edition, vol.38, issue.12, pp.1784-1788, 1999.
DOI : 10.1002/(SICI)1521-3773(19990614)38:12<1784::AID-ANIE1784>3.0.CO;2-Q
E. Vinogradov, K. Bock, O. Holst, and H. Brade, The Structure of the Lipid A-Core Region of the Lipopolysaccharides from Vibrio cholerae O1 Smooth Strain 569B (Inaba) and Rough Mutant Strain 95R (Ogawa), European Journal of Biochemistry, vol.1, issue.1, pp.152-158, 1995.
DOI : 10.1016/0008-6215(92)84007-F
A. Van-roon, N. Pannu, J. De-vrind, G. Van-der-marel, J. Van-boom et al., Structure of an Anti-Lewis X Fab Fragment in Complex with Its Lewis X Antigen, Structure, vol.12, issue.7, pp.1227-1236, 2004.
DOI : 10.1016/j.str.2004.05.008


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Home News Manika Batra, G. Sathiyan Seeded 1st At The National Games
Manika Batra, G. Sathiyan Seeded 1st At The National Games
Saikat Banerjee
Image Courtesy: AFP/PTI
Indian table tennis stalwarts Manika Batra and G. Sathiyan are heading into the upcoming 36th National Games as the No. 1 seeds in women's and men's singles categories respectively.
A decision from the jury, comprised of T G Upadhya, A S Kler, and Anil Dubey, was made on Tuesday, with Sathiyan and Manika beating Sharath Kamal and Akula Sreeja to secure the top spot in the men's and women's singles, pushing both the national champions down to the second spots.
In the recent Birmingham Commonwealth Games, Sharath went on to win four medals, including three golds, but his world ranking at No. 43 pushed him down a rung. In contrast, Sathiya, ranked 37, is the number one Indian in the world.
In the same way, Manika also benefited from the same rule, as she is now ranked 45, while Sreeja is ranked 77. National rankings were put ahead of world rankings for seeding by the jury.
Manika, who clinched four medals including two two gold at the 2018 CWG, disappointed at the 2022 edition. However, the 27-year-old has vowed to make a strong comeback at the National Games.
"Of course, I was sad and upset when I lost my matches in CWG but I always tell myself this is not the end of the world," said Manika during a virtual interaction, "2018 was really an amazing year for me. This time, I played best before the CWG. I did really well in the World Tours, beat good players."
"I think I played my best in CWG but every player has ups and down in their lives. It's not finished for me just because I didn't get a medal at the CWG," the top-ranked Indian added. [H/T Sportstar]
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Eisteddfod Gadeiriol Llandyfaelog
History of Llandyfaelog
Llandyfaelog Community Hall
Community Hall News
Community Hall Committee
Community of Llandyfaelog
Llandyfaelog is a very rural community made up of the villages of Llandyfaelog and Cwmffrwd, the hamlets of Glanmorlais, Idole, Pentrepoeth, Croesyceiliog, Bancycapel and Cloigyn. These are separated by large swathes of farmland.
According to the 2011 census the Community has a population of 1304 of which 56.8 percent are Welsh speaking.
Religious services are held at the four chapels and two churches. There are several businesses located in the area including a car showroom. There is one Public House, the Red Lion in Llandyfaelog
Social, cultural and community groups feature prominently in the area. Annual events include the Llandyfaelog Eisteddfod in November and the Llandyfaelog Agricultural and Horticultural show in September. Other events are held throughout the year by the Llandyfaelog Community Hall Committee, St Ishmael's Young Farmers Club, Pensioners Association, Women's Institute and Merched y Wawr.
Facilities for holiday makers include several Bed and Breakfasts and caravan parks. There are also several challenging footpaths for the adventurous walker!
Llandyfaelog Community Council Privacy Notice
© Llandyfaelog Community Council. All Rights Reserved. Design by Vision ICT Ltd - Accessibility Statement .


Abstract base class that defines the interface that every trigger must implement.
Returns the next datetime to fire on, If no such datetime can be calculated, returns None.
© Copyright Alex Grönholm. Revision 93c43932.


In a confirmation of regulatory uncertainty, the U.S. Securities and Exchange Commission (SEC) just officially announced the further delay of bitcoin's most anticipated exchange-traded fund (ETF).
VanEck, SolidX, and the Chicago Board Options Exchange (CBOE) will need to wait until at least February 2019 to see their Bitcoin ETF approved.
The SEC has decided to postpone a proposed rule change, allowing for the trade of VanEck-SolidX's bitcoin shares, according to an SEC notice today.
Earlier this year in June, the CBOE suggested a change in SEC guidelines, allowing VanEck-SolidX bitcoin shares to be listed and traded as Commodity-Based Trust Shares on CBOE's BZX exchange.
Two months later, the SEC stated they needed more time to review the situation. September saw the same fate, as the ruling body pushed the decision further into the future.
The most recent September delay surfaced doubts from the SEC on the topic of bitcoin manipulation and maturity.
As of the December 6th notice, over 1600 responses have been sent to the SEC regarding the rule change in question.
The SEC will approve or deny the rule change on February 27, 2019.
The SEC has received many bitcoin ETF applications this year, approving none of them. Back in August, CNBC reported on nine bitcoin ETF rejections.
The crypto community remains hopeful, looking for a bitcoin ETF to turn the market around. Such a financial instrument would allow for larger market players to gain significant exposure to crypto assets if they so choose.
Popular crypto influencer Nicolas Merten, aka Data Dash, tweeted about the importance of a bitcoin ETF, and its impact on the crypto space.
With the release of an ETF, this allows investors to add bitcoin to their retirement portfolio.
If bitcoin captures just 1% of global pensions, that would create $413,000,000,000 of exposure for cryptocurrencies.
Although the crypto space has remained hopeful, the SEC has made it clear they do not think the crypto market is ready for such a product right now.
*CryptoInsider is sponsored by Blockmodo. As part of our arrangement, we may occasionally link to them and quote them when appropriate. This is done at the discretion of CI staff and CI sponsors have no say in any editorial decisions made by CI.


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Determined to discover the truth about the birth mother who abandoned her, Amelia Crenshaw soon discovers that she is on a collision course with a killer and a brash young NYPD detective whose case is connected to a long-ago crime and her search.
SKU: 9780062742056 Genres: Contemporary, Suspense, Thriller. Author: Staub Wendy Corsi.


text:
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This article is from the Encyclopedia of North Carolina edited by William S. Powell. Copyright © 2006 by the University of North Carolina Press. Used by permission of the publisher. For personal use and not for further distribution. Please submit permission requests for other use directly to the publisher.
Granite is the official State Rock of the State of North Carolina. It was designated as the State Rock in 1979 by the N.C. General Assembly.
by Jean H. Seaman, 2006
See also: North Carolina State Symbols and Official Adoptions main page
Granite is a massive igneous rock with a crystalline texture containing feldspar, quartz, and one or more dark iron silicate or ferromagnesian minerals, usually biotite or hornblende. The granite in North Carolina is described as first in quality in the southeastern states and second in quantity to that of Georgia. In 1980 the state's granite production was sixth in the United States, with ten quarries in five counties: Avery, Mitchell, Rowan, Surry, and Watauga. The earliest recorded granite quarry was in Wake County southeast of Raleigh about 1805 for local use. A second quarry east of Raleigh in a granite gneiss supplied stone for building the State Capitol in 1833-36. Rowan County had quarrying near Salisbury before the Civil War, as did Davidson County near Lexington for the courthouse in 1856 and piers for a railroad bridge. Stone for Davidson College was excavated in Iredell County in the same era.
Modern quarrying methods and improved transportation to markets encouraged expansion of the industry in the late nineteenth century with numerous openings extended or begun. Crushed stone from scrap has been sold for road metal as an important complement to dimension stone production.
Types of granite found in North Carolina include Balfour pink, a rose to pink granite from Rowan County that has been sold as "Salisbury pink" and "Arabian pink" since before 1860; gabbro, dark gray to black granite with dark minerals predominant that was quarried by the Old Consolidated Granite Company in Rowan County as black granite about 1902 and shipped as monument stock to locations up and down the eastern seaboard; orbicular diorite, granite in which dark green nodules of hornblende occur in a white matrix of feldspar, which is unique to Davie County near the Yadkin River; and Mount Airy granite, a white to light gray biotite granite of uniform texture and color found in Surry County northeast of the city of Mount Airy. In 1979 granite was designated as the official state rock.
R. J. Councill, Commercial Granites of North Carolina, North Carolina Geological Survey Bulletin no. 67 (1954).
U.S. Bureau of Mines, Minerals Yearbook: The Mineral Industry of North Carolina (1980).
"An act to adopt granite as the official rock for the state of North Carolina." 1979. Session Laws. c. 906. Online at: http://digital.ncdcr.gov/u?/p249901coll22,389284
North Carolina Geological Survey Section; North Carolina Department of Natural Resources and Community Development. Geologic Guide to North Carolina's State Parks. Raleigh, N.C.: North Carolina Geological Survey Section. 1989. http://digital.ncdcr.gov/cdm/ref/collection/p16062coll9/id/13533 (accessed December 10, 2014).
UNC Press
Seaman, Jean H.
Origin - location:
Avery County
Encyclopedia of North Carolina, University of North Carolina Press.
1 January 2006 | Seaman, Jean H.
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SBA Economic Injury Disaster Loans Available in New York Following Secretary of Agriculture Disaster Declaration.
The U.S. Small Business Administration announced today that Economic Injury Disaster Loans are available to small businesses, small agricultural cooperatives, small businesses engaged in aquaculture and private nonprofit organizations located in New York as a result of rain, flash flooding that began on July 21, 2018.
Allegany, Broome, Cattaraugus, Chautauqua, Steuben and Tioga in New York. "These counties are eligible because they are contiguous to one or more primary counties in Pennsylvania. The Small Business Administration recognizes that disasters do not usually stop at county or state lines.
Columbia, Dutchess and Rensselaer counties of New York are eligible because they are contiguous to one or more primary counties in Massachusetts.
Completed loan applications must be submitted to SBA no later than Nov. 20, 2019.
NY 15718/15719 - SBA Offers Disaster Assistance to New York Residents affected by severe storms and flooding in Broome, Cayuga, Chenango, Cortland, Delaware, Ontario, Schuyler, Seneca, Tioga, Tompkins, Wayne and Yates Counties of New York.
Details: The U.S. Small Business Administration (SBA) announces that businesses and residents affected by severe storms and flooding occurring August 13-14, 2018 can apply for low-interest disaster loans.
The declaration covers Broome and Seneca and the contiguous counties of Cayuga, Chenango, Cortland, Delaware, Ontario, Schuyler, Tioga, Tompkins, Wayne and Yates in New York.
Survivors may apply online using the Electronic Loan Application (ELA) via SBA's secure website at https://disasterloan.sba.gov/ela.
Businesses and individuals may also obtain information and loan applications by calling the SBA's Customer Service Center at 1-800-659-2955 (1-800-877-8339 for the deaf and hard-of-hearing), or by emailing disastercustomerservice@sba.gov.
The filing deadline to submit applications for physical property damage is December 10, 2018. The deadline for economic injury applications is July 9, 2019.