In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

n this section, we first describe the phenomenon of mention of political parties names in the profile attributes of users. This is followed by the analysis of profiles that make specific mentions of political handles in their profile attributes. Both of these constitute an organic way of showing support to a party and does not involve any direct campaigning by the parties We opine that the whole campaign of adding Chowkidar to the profile attributes show an inorganic behavior, with political leaders acting as the catalyst. We believe, the effect of changing the profile attribute in accordance with Prime Minister's campaign is an example of inorganic behavior contagion BIBREF6, BIBREF9. However, we argue that the addition of election campaign related keywords to the profile is a form of inorganic behavior.
What are the organic and inorganic ways to show political affiliation through profile changes?

As a document classifier we employ a word-based CNN similar to Kim consisting of the following sequence of layers: $ \texttt {Conv} \xrightarrow{} \texttt {ReLU} \xrightarrow{} \texttt {1-Max-Pool} \xrightarrow{} \texttt {FC} \\ $ Future work would include applying LRP to other neural network architectures (e.g. character-based or recurrent models) on further NLP tasks, as well as exploring how relevance information could be taken into account to improve the classifier's training procedure or prediction performance.
Do the experiments explore how various architectures and layers contribute towards certain decisions?

We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2).
What kind of model do they use for detection?