You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: Instagram hashtags can be considered as good candidates for image annotation.

Keyphrases: automatic image annotation images

Dataset description:
IG-3.5B-17k: IG-3.5B-17k is an internal Facebook AI Research dataset for training image classification models. It consists of hashtags for up to 3.5 billion public Instagram images.
Flickr30k: The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators.
ImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.
InstaFake: Includes two datasets published for the detection of fake and automated accounts.
NELL: NELL is a dataset built from the Web via an intelligent agent called Never-Ending Language Learner. This agent attempts to learn over time to read the web.
MDID: The Multimodal Document Intent Dataset (MDID) is a dataset for computing author intent from multimodal data from Instagram. It contains 1,299 Instagram posts covering a variety of topics, annotated with labels from three taxonomies.

Options:- A: IG-3.5B-17k  B: Flickr30k  C: ImageNet  D: InstaFake  E: NELL  F: MDID
["Flickr30k", "ImageNet"]