{'fraction_non_alphanumeric': 0.015873015873015872, 'fraction_numerical': 0.000992063492063492, 'mean_word_length': 5.130699088145897, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Comparative Study of Deep Learning Methods for Spam Detection

Since the last decade, internet plays an imperative and vital role in the creation and retrieval of colossal amounts of information. With ever-increasing advancements in technological field and creation of data at an exponential rate, impertinent or irrelevant data is proliferating at a vast scale in commensuration with relevant data. Moreover, the usage of mobile phones has increased drastically, and phones are becoming an evident part of everyone's lives. With this, there is a notable increase in the number of spam messages from spammers. According to recent statistics, 96% of Indians receive unsolicited text messages every day. SMS spam is any unwanted or unsolicited text note in the form of weblink, promotional message or irrelevant text sent uncritically and non-selectively to your mobile phone, regularly for advertising purposes. The surge in unsolicited information across all platforms including mobile text messages and emails has created an expedited need for the advancement and refinement of more reliable filters to counteract the spam in these messages. Traditionally, rule-based approach is employed to counteract spam messages. According to this approach, a set of rules are employed on the messages by some authority manually. By this method, no favorable or assuring results will be shown because the rules need to regularly be restructured based on the source of spam messages, which is an arduous process. Instead, we use deep learning methods that are efficient and does not require any rules. Deep learning models require a set of training dataset samples to learn the rules from these SMS messages and build a text classifier that efficiently classifies spam from these messages. This paper presents a systematic review of employing deep learning methods namely, convolutional neural network and recurrent neural network on huge corpus of SMS texts to build a spam classifier that classifies messages as ham or spam.