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Internet of Things integrates objects with unique addressing schemes to their digital processing platforms via communicating network to create advanced control application systems with minimum human interaction. Internet of Things network is expected to have a device/object base of around 212 billion by the year 2020. Such a huge base of devices demands special security mechanisms. The security lags of the hardware and software components of the devices involved comprise to the overall security threat to the Internet of Things paradigm. Internet of Things in its basic form is a three-layered architecture, having physical, network and application layers. This paper discusses the potential attacks on the Internet of Things at each layer of its basic architecture while providing insights about their severity and impact over an Internet of Things network. The paper further elaborates on solutions about the physical security of the Internet of Things devices under a constrained environment and the need for security in the network section where protocols for routing and encryption are implemented. The paper also reviews the solutions at the application level focusing on the valid access to data available on mobile and web interface. Finally, the paper concludes with the discussion regarding the current security scenario and future directions.

The automatic extraction of breaking news events from natural language text is a valuable capability for decision support systems. Traditional systems tend to focus on extracting events from a single media source and often ignore cross-media references. Here, we describe a large-scale automated system for extracting natural disasters and critical events from both newswire text and social media. We outline a comprehensive architecture that can identify, categorize and summarize seven different event types - namely floods, storms, fires, armed conflict, terrorism, infrastructure breakdown, and labour unavailability. The system comprises fourteen modules and is equipped with a novel coreference mechanism, capable of linking events extracted from the two complementary data sources. Additionally, the system is easily extensible to accommodate new event types. Our experimental evaluation demonstrates the effectiveness of the system.

In this paper, we present a new approach for detecting novel events from social media, specially Twitter, at real-time. An event is usually defined by who, what, where and when, and an event tweet usually contains terms corresponding to these aspects. To exploit this information, we propose a method that incorporates simple semantics by splitting the tweet term space into groups of terms that have the meaning of the same type. These groups are called semantic categories (classes) and each reflects one or more event aspects. The semantic classes include named entity, mention, location, hashtag, verb, noun and embedded link. To group tweets talking about the same event into the same cluster, similarity measuring is conducted by calculating class-wise similarity and then aggregating them together. Users of a real-time event detection system are usually only interested in novel (new) events, which are happening now or just happened a short time ago. To fulfill this requirement, a temporal identification module is used to filter out event clusters that are about old stories. The clustering module also computes a novelty score for each event cluster, which reflects how novel the event is, compared to previous events. We evaluated our event detection method using multiple quality metrics and a large-scale event corpus having millions of tweets. The experiment results show that the proposed online event detection method achieves the state-of-the-art performance. Our experiment also shows that the temporal identification module can effectively detect old events.

Previous studies have shown that investor sentiment indicators can predict stock market change. A domain-specific sentiment lexicon and sentiment-oriented word embedding model would help the sentiment analysis in financial domain and stock market. In this paper, we present a new approach to learning stock market lexicon from StockTwits, a popular financial social network for investors to share ideas. It learns word polarity by predicting message sentiment, using a neural net-work. The sentiment-oriented word embeddings are learned from tens of millions of StockTwits posts, and this is the first study presenting sentiment-oriented word embeddings for stock market. The experiments of predicting investor sentiment show that our lexicon outperformed other lexicons built by the state-of-the-art methods, and the sentiment-oriented word vector was much better than the general word embeddings.

Social media hashtags are useful in many applications, such as tweet classification, clustering, searching, indexing, and social network analysis. In this chapter, we present a Big Data mining technology on social media, and demonstrate how to use it to address the following three problems: discovering relevant hashtags for health concepts, discovering the meaning of health-related hashtags, and identifying hashtags relevant to each other in the health domain. The proposed approach is based on the distributed word representations, which are learned, by applying the state-of-the-art deep learning technology, from billions of tweet words without supervision. The experiment shows that this approach outperformed the baseline approach. To the best of our knowledge, this is the first study of applying distributed language representations to discovering relationships between health concepts and hashtags.

This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs. We use three types of word embeddings in our algorithm: word embeddings learned from 200 million tweets, sentiment-specific word embeddings learned from 10 million tweets using distance supervision, and word embeddings learned from 20 million StockTwits messages. In our approach, we also take the left and right context of the target company into consideration when generating polarity prediction features. All the features generated from different word embeddings and contexts are integrated together to train our algorithm

This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but it is still a challenging research task. In our approach, we take the left and right context of a target into consideration when generating polarity classification features. We use two types of word embeddings in our classifiers: the general word embeddings learned from 200 million tweets, and sentiment-specific word embeddings learned from 10 million tweets using distance supervision. We also incorporate a text feature model in our algorithm. This model produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. We participated in four subtasks (B, C, D & E for English), all of which are about topic-based message polarity classification. Our team is ranked #6 in subtask B, #3 by MAEu and #9 by MAEm in subtask C, #3 using RAE and #6 using KLD in subtask D, and #3 in subtask E.

News professionals are facing the challenge of discovering news from more diverse and unreliable information in the age of social media. More and more news events break on social media first and are picked up by news media subsequently. The recent Brussels attack is such an example. At Reuters, a global news agency, we have observed the necessity of providing a more effective tool that can help our journalists to quickly discover news on social media, verify them and then inform the public. In this paper, we describe Reuters Tracer, a system for sifting through all noise to detect news events on Twitter and assessing their veracity. We disclose the architecture of our system and discuss the various design strategies that facilitate the implementation of machine learning models for noise filtering and event detection. These techniques have been implemented at large scale and successfully discovered breaking news faster than traditional journalism

Classifying tweets into topic categories is necessary and important for many applications, since tweets are about a variety of topics and users are only interested in certain topical areas. Many tweet classification approaches fail to achieve high accuracy due to data sparseness issue. Tweet, as a special type of short text, in additional to its text, also has other metadata that can be used to enrich its context, such as user name, mention, hashtag and embedded link. In this demonstration, we present TweetSift, an efficient and effective real time tweet topic classifier. TweetSift exploits external tweet-specific entity knowledge to provide more topical context for a tweet, and integrates them with topic enhanced word embeddings for topic classification. The demonstration will show how TweetSift works and how it is incorporated with our social media event detection system.

In this paper, we present a new approach of recommending hashtags for tweets. It uses Learning to Rank algorithm to incorporate features built from topic enhanced word embeddings, tweet entity data, hashtag frequency, hashtag temporal data and tweet URL domain information. The experiments using millions of tweets and hashtags show that the proposed approach outperforms the three baseline methods -- the LDA topic, the tf.idf based and the general word embedding approaches.

Social networks enable knowledge sharing that inevitably begs the question of expertise analysis. Many online profiles claim expertise, but possessing true expertise is rare. We characterize expertise as projected expertise (claims of a person), perceived expertise (how the crowd perceives the individual) and true expertise (factual). StockTwits, an investor-focused microblogging platform, allows us to study all three aspects simultaneously. We analyze more than 18 million tweets spanning 1700 days. The large time scale allows us to also analyze expertise and its categories as they evolve over time, which is the first study of its kind on StockTwits. We propose a method to capture perceived expertise by how significantly a user's follower network grows and how often the user is brought up in conversations. We also quantify actual, market-based, true expertise based on the user's trade and investment recommendations. Finally we provide an analysis bringing out the differences between how users project themselves, how the crowd perceives them, and how they are actually performing on the market. Our results show that users who project themselves as experts are ones that talk the most and provide the least recommendation-to-tweet ratio (that is, most of their conversations are mundane). The recommendations from users who project novice expertise slightly outperform (≈5%) the overall stock market. On the other hand, the trade recommendations from self-proclaimed experts yield 80% less than those of intermediate traders. Interestingly, users who are perceived as experts by others, as measured by centrality measurements, resulted in net negative returns after a four year trading period. Our study also looks at the evolution of expertise, and begins to understand why and what makes users change the way they project their own expertise. For this topic, however, this paper introduces more questions than it answers, which will serve as the basis for future studies.