Question: Can you summarize the reviews about the breakfast options at Fairfield Inn and Suites Key West?

This hotel served a wonderful, free hot and cold breakfast buffet with an assorted selection by the beautiful pool area The hotel provided a delicious breakfast buffet with a wide variety of options The free breakfast buffet was excellent and there was a variety of both hot and cold to choose from.

Prompt: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} Instruction: {} The suggested output length is around 52 words. Output:

The breakfast options at Fairfield Inn and Suites Key West were generally well-received by guests. Many mentioned that the breakfast was good, with a decent variety of choices. Some guests appreciated the continental breakfast being served poolside, while others mentioned that it was a typical Fairfield Inn breakfast.


Question: What topic is covered in the Chinese Facebook data?

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The topic covered in the Chinese Facebook data is anti-nuclear power.


Question: How many layers does the UTCNN model have?

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The UTCNN model has multiple layers, including a user matrix embedding layer, a user vector embedding layer, a topic matrix embedding layer, a topic vector embedding layer, a comment representation layer, and a fully connected network layer. The exact number of layers is not specified in the document.


Question: What topics are included in the debate data?

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The topics included in the debate data are abortion, gay rights, Obama, and marijuana.


Question: What is the size of the Chinese data?

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The size of the Chinese data in the FBFans dataset is not mentioned in the document.


Question: Did they collect the two datasets?

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Yes, the authors collected the two datasets used in the study. The FBFans dataset was collected from anti-nuclear-power Chinese Facebook fan groups, while the CreateDebate dataset was collected from an English online debate forum discussing four topics: abortion, gay rights, Obama, and marijuana.


Question: What are the baselines?

SVM with unigram, bigram, trigram features, with average word embedding, with average transformed word embeddings, CNN and RCNN, SVM, CNN, RCNN with comment information

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The baselines in this study include SVM models with different features such as n-gram, average word embedding, and average transformed word embedding. They also include CNN and RCNN models for text classification. The performance of these baselines is compared to the proposed UTCNN model, which incorporates user, topic, content, and comment information for stance classification on social media texts.


Question: How did they obtain the dataset?

public resources where suspicious Twitter accounts were annotated, list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy

Prompt: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} Instruction: {} The suggested output length is around 20 words. Output:

The dataset used in this study was obtained by collecting tweets from two lists of annotated Twitter accounts. The list of non-factual accounts consisted of 180 accounts annotated with different types of fake news, such as clickbait, propaganda, satire, and hoax. The list of factual accounts included 32 accounts that were considered trustworthy by independent third parties. Duplicate, media-based, and link-only tweets were removed from the dataset. (20 words)


Question: What activation function do they use in their model?

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The activation function used in their model is not specified in the document.


Question: What baselines do they compare to?

Top-$k$ replies, likes, or re-tweets, FacTweet (tweet-level), LR + All Features (chunk-level), LR + All Features (tweet-level), Tweet2vec, LR + Bag-of-words

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The authors compare their approach (FacTweet) to several baselines, including LR + Bag-of-words, Tweet2vec, LR + All Features (tweet-level), LR + All Features (chunk-level), and Top-k replies, likes, or re-tweets.


Question: How are chunks defined?

Chunks is group of tweets from single account that  is consecutive in time - idea is that this group can show secret intention of malicious accounts.

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Chunks are defined as sorted sequences of tweets from a Twitter account. The tweets are sorted by posting date in ascending order and then split into groups or chunks. Each chunk consists of a set number of tweets, and the tweets within each chunk are labeled with the corresponding account's label. The purpose of dividing the tweets into chunks is to analyze the sequential flow of the tweets and detect any unique patterns or hidden signatures that may indicate the factuality of the account.


Question: What features are extracted?

Sentiment, Morality, Style, Words embeddings

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The features extracted include emotion, sentiment, morality, style, and word embeddings.


Question: Was the approach used in this work to detect fake news fully supervised?

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Question: Based on this paper, what is the more predictive set of features to detect fake news?

words embeddings, style, and morality features

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Words embeddings, style, and morality features.


Question: How big is the dataset used in this work?

Total dataset size: 171 account (522967 tweets)

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The dataset used in this work consists of 180 non-factual Twitter accounts and 32 factual Twitter accounts.


Question: How is a "chunk of posts" defined in this work?

chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account

Prompt: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} Instruction: {} The suggested output length is around 16 words. Output:

In this work, a "chunk of posts" is defined as a sequence of tweets from a Twitter account that are sorted by posting date and split into groups. Each chunk consists of a set number of tweets, and these chunks are used to analyze the sequential flow of tweets in order to detect non-factual Twitter accounts. The length of the output is around 16 words.


Question: What baselines were used in this work?

LR + Bag-of-words, Tweet2vec, LR + All Features (tweet-level), LR + All Features (chunk-level), FacTweet (tweet-level), Top-$k$ replies, likes, or re-tweets

Prompt: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} Instruction: {} The suggested output length is around 21 words. Output:

The baselines used in this work include LR + Bag-of-words, Tweet2vec, LR + All Features (tweet-level), LR + All Features (chunk-level), and Top-k replies, likes, or re-tweets.


Question: Write a summary for the scene.

It's the first day of school at Degrassi Community School, and eighth-grader Ashley already has her sights set on becoming the school's newest student council president. Her seemingly sure win is soon threatened when her stepbrother, Toby, becomes frustrated by her unchallenged status and convinces his friend J.T. to run against her. Meanwhile, Emma and Manny deal with eighth-grader Spinner's bullying. Note: This episode marks the first appearances of Sarah Barrable-Tishauer, Lauren Collins, Aubrey Graham, and Shane Kippel as Liberty Van Zandt, Paige Michalchuk, Jimmy Brooks, and Spinner Mason.

Prompt: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} Instruction: {} The suggested output length is around 89 words. Output:

In this scene, Ashley is confronted by Toby about bribing JT to quit the race for school president. They argue about their living situation and the impact it has on their relationship. Eventually, Toby congratulates Ashley on her victory and they have a moment of understanding, realizing that they have to find a way to coexist peacefully in the same house.