Glossary Adjacency matrix Adjective Orientation Similarity Aspect coverage Bipartite networks CAO Collaborative filtering Complete graph

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1 Glossary Adjacency matrix The adjacency matrix is a matrix whose rows and columns represent the graph vertices. A matrix entry at position (i, j) contains a 1 or a 0 value according to whether an edge is present between the nodes i and j Adjective Orientation Similarity The adjective orientation similarity evaluates the semantic orientation similarity of all the adjective terms between two given sentences Aspect coverage Aspect coverage can be defined as the percentage of topic aspects covered by the summary of reviews Bipartite networks A bipartite network is a set of network nodes divided into two disjoint sets such that no links are present between two nodes within the same set CAO An affect analysis system for emoticons created by Michal Ptaszynski Collaborative filtering collaborative filtering is one common algorithm used for building recommender systems. It tries to predict the utility of an item for a particular user based on the ratings on this item given by other similar users, or the ratings on similar items given by this user; the former is called user-based collaborative filtering, and the latter is called item-based collaborative filtering. (Toward the next generation of recommender systems: a survey of the state of the art and possible extensions, by G. Adomavicius, and A. Tuzhilin, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, Issue 6, pp , June 2005) Complete graph A complete graph is a graph where each pair of vertices is linked by an edge Computer-mediated communication A way of communication between humans that occurs through the use of two or more electronic devices Confusion matrices Given a classification model, the confusion matrix indicates in which way the predictions are performed by the model. The rows represent the known classes of the data, i.e., the class labels. The columns are the classes predicted by the model. The value of a matrix entry at position (i, j) corresponds to the number of data items with known class i and predicted class j Connected components The connected components of a graph represent the set of largest subgraphs, where any two vertices are linked to each other by paths, and which are not connected to other vertices in the original graph Springer International Publishing Switzerland 2015 Ö. Ulusoy et al. (eds.), Recommendation and Search in Social Networks, Lecture Notes in Social Networks, DOI /

2 282 Glossary Connectivity Value Connectivity value structurally represents a user and its connection in the network Crawling Model Conmponents and their relationships for systematically browsing the World Wide Web (Twitterverse in this case) Dynamic Network Dynamic network is an architecturally variable network that conforms to the change in the attributes of the entities in the network Edge weight Edge-weight is the measure of the strength of the relation between the users at either end of the edge. It is computed as a weighted sum of the communication and recommendation flowing through that edge Edge-source The end of the edge or the link that initiates the communication is called as the source Edge-target The end of the edge or the link that listens to the communication is called as the target Emoticons Facial marks or movements that are composed of letter and used in text messages Evolutionary Principle Incorporating temporal changes by being true to the present and not deviating dramatically from the past Geodesic A geodesic of a graph G is a shortest path between two vertices (u,v)of G. The length of the maximum geodesic in G is the graph diameter, the length of the minimum geodesic is defined as the graph radius Glastonbury festival A five-day music festival that takes place near Pilton, Somerset, England Growth rate It is the ratio of the number of users influenced in a time window to the number of non-influenced users in that time window Influence flow Influence flow is the spread of influence through the edges/links in the network Influence value Influence value is computed as a function of the number of influenced neighbors and the strength of the relation between them Influence Influence is the state where a user starts using a product or service because of direct or viral marketing Intrinsic Value A normalized score calculated as a composition of various attributes of a user in reference to the marketable product or service Jaccard similarity Jaccard similarity coefficient measures similarity between two finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets. (Wikipedia) Joint entropy The joint entropy of two discrete random variables X and Y, with joint probability mass function p(x, y) is defined as H(X, Y ) = p(x, y)log p(x, y). x X y Y Kinesics An interpretation of body motion communication such as facial expressions and gestures

3 Glossary 283 Live edge An edge in a network is a live edge if the source of the edge is influenced and the target is not Maximal complete subgraphs A maximal complete subgraph of a graph G is a complete subgraph of G which is not properly included in another complete subgraph of G Maximal frequent sharing patterns A frequent sharing pattern without a proper superset that is frequent ML-Ask An affect analysis system of textual input in Japanese based on a linguistic assumption that emotional states of a speaker are conveyed by emotional expressions used in emotive utterances. The system was created by Michal Ptaszynski Music recommender system Music recommender system recommends music to users based on their preferences, interests, or other related information, the commonly used algorithms include content-based, collaborative filtering, and hybrid. (Toward the next generation of recommender systems a survey of the state of the art and possible extensions, by G. Adomavicius, and A. Tuzhilin, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, Issue 6, pp , June 2005) Mutual information The mutual information is the uncertainty reduction in one random variable given the knowledge about the other one. If the mutual information is high, it represents a huge reduction in uncertainty; if the mutual information is low, it indicates a small reduction; if the mutual information between the two random variables is zero, it means that the variables are independent. Given two discrete variables X and Y with joint probability distribution P X,Y (x, y), the mutual information between them is I (X; Y ) = x,y P X Y (x, y)log P XY(x,y) P X (x)p Y (y) = E PXY log P XY P X P Y, where P X (x) and P Y (y) are the marginals, P X (x) = y P XY (x, y) and P Y (y) = x P XY(x, y) and E P is the expected value over the distribution P Network Value Network value is the measure of a user s capacity as an influencer for a product or service in the network. It is a function of the intrinsic value and the connectivity value of a user for that product or service Nonuniform Random Walks A random walk whose next hop is not selected uniformly at random out of the available choices Online Paid Posters Users who get paid to write promotional or fake reviews and comments online Orthogonal matrix A n n matrix A is an orthogonal matrix if AA T = I, where A T is the transpose of A and I is the identity matrix Patterns Patterns are consistent and recurring features that help to model a phenomenon or problem, and are useful as indicators or models for predicting its future trend Pearson correlation A measure of the linear correlation (dependence) between two variables X and Y in statistics Pearson correlation is a measure of the linear correlation between two variables X and Y, giving a value between +1 and 1 inclusive, where 1 is the total positive correlation, 0 is no correlation, and 1 is total negative correlation. (Wikipedia)

4 284 Glossary PerSocial Relevance A relevance model that determines the social relevance between a user and a document PerSocialization Personalization of search results using social signals Personalized Search Engine (PERSOSE) Search engine that uses social signals to personalize the search results Polarity distribution preservation Polarity distribution preservation evaluates the correlation of aspect-level polarity between reviews and the system generated summary Precision In the context of information retrieval, precision is the fraction of the retrieved documents which are relevant. (Modern Information Retrieval: The Concepts and Technology behind Search (2nd Edition), Ricardo Baeza-Yates, and Berthier Ribeiro-Neto, Addison Wesley, 2010, ISBN ) Predictive Accuracy Measures how close a predicted value (given by the Recommender System) is to a withheld actual rating Rate limit An upper limit set by Twitter that used to control the rate of requests per user Recall In the context of information retrieval, recall is the fraction of the relevant documents which have been retrieved by the information retrieval system. (Modern Information Retrieval The Concepts and Technology behind Search (2nd Edition), Ricardo Baeza-Yates, and Berthier Ribeiro-Neto, Addison Wesley, 2010, ISBN ) Recommendation List Diversity Measures how different the items of a recommendation list are from one another Recommendation List Novelty Measures the extent to which an item (or a set of items) is new when compared with those items that have already been consumed by a user (or a community of users) Recommendation score It is an average score of the user and its connections to recommend a product or service in the network Recommender Systems Software systems that aim to propose new items that have not been evaluated by the users yet Rejection Sampling A statistical technique for generating samples from a hard-tosample distribution by employing as an instrument an easy-to-sample distribution Representatives A representative is an individual who represents a constituency or community in a legislative structure, i.e., a member of the US House of Representatives Requent sharing pattern A combination of vertex labels that is shared within a connected subgraphs with a minimum number of vertices Roll calls The roll calls are voting processes where legislators are called on by name and have the possibility to cast their vote or to abstain SE (Search Engine) Search engines are services that crawl very large amount of data (documents or websites, for example) and can efficiently search them for keywords to return a list of matching documents Shortest paths The shortest path between two vertices i and j is a path such that the sum of the weights of its edges is minimized with respect to the other possible paths between them. For unweighted graphs, every edge is weighted as 1

5 Glossary 285 SMQA (Social Media Question Asking) Often people use social networking sites to ask queries to their network members, or to generic people using that service. Researchers have termed this as social media question asking (SMQA). The social networking site concerned may be of general purpose (e.g., Facebook, Twitter) or provide specific type of service (for example, Jelly) SNS (Social Networking Sites) Services that enable the users to build and use social connectivity with other users. The concept of social networks predates the computer era. But the widespread penetration of the Internet, especially with proliferation of mobile computing devices has made the implementation of social networking sites a success. Common examples are Facebook, Twitter, Google+, Weibo, etc. Social Actions Set of actions that a given user can perform on any document. Examples include LIKE, RECOMMEND, and SHARE Social network A social network is defined as a network of interactions or relationships, where the nodes consist of actors, and the edges consist of the relationships or interactions between these actors. (Social Network Data Analytics, edited by Charu C. Aggarwal, ISBN: , Springer, 2011) Social tagging Tagging is a process where a user assigns a tag to a web object or resource; social tagging is to tag the object during the social interactions supported by the social networking site. (Social Network Data Analytics, edited by Charu C. Aggarwal, ISBN: , Springer, 2011) Spam Detection Use machine learning techniques to identify the potential online paid posters Strongly-Connected group A group of users within a larger network having a strong association within the group than outside is termed as a strongly connected group. The association is represented by the weight of the edges connecting them Supervised Learning Machine learning task of inferring a function from labeled training data Temporal update Temporal update is the incorporation of the temporal changes in the network TF-IDF Short for term frequency-inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus Training Data Manually labelled samples Trust Networks Social networks that are comprised of trust statements among the actors Twitter API Twitter is an online social networking service that enables users to send and read short 140-character messages called tweets. The Twitter s application programming interface (API) allows other Web services and applications to integrate with Twitter Twittersphere/Twitterverse The entire Twitter world, especially the postings made on the social media website Twitter, considered collectively Unsupervised Learning Find hidden structure in unlabeled data

6 286 Glossary Viral Marketing Viral Marketing is a marketing methodology that relies on getting the customers of a product or service to promote it to their connections in the network Virtual Network It is a network comprising of digital links between the entities within a network. Entities communicate with each other via these links. The links represent the relation between the entities Voting records Voting records are lists containing the voting history of candidates or elected officials

7 Index A Affect analysis system, 25, 26, 37 Agreeableness, 71 Agreement index, 252 k-anonymous, 79, 83, 86, 88, 91, 98 Average degree, 260 B Betweenness centrality, 263 Betweenness centralization, 263 Biased random walk, 41, 45, 46 Binarized matrix, 258 Bipartisan coordinate, 253, 274 Bipolar words, 29, 30 C Cliques, 265, 276 Clustering coefficient, 263, 264 Cold-start users, 49 51, 55 Collaborative filtering, 120, 121, 124, 130 Computer-mediated communication, 24 Connectivity value, Conscientiousness, 70 Consensus, 63 Correlation coefficient, 45 Crawler, 1, 5, 7, 9 14, Credibility, 60 Digital divide regarding information, 191 Dynamic network, 219, 246 E Edge weight, 223, 225, 227, 232, 237, 241 Effect sizes, 72 Emoticon, 23 32, 34, 37, 38 Emoticon database, 25, 26, 31, 37, 38 Emoticon dictionary, 24 Emoticon recommendation methods, 24 26, 29 Emotive word, 30, 38 Evolutionary, 220, 221, 228, 229, , 237, 238, 241, 245, 246 Experiment, , 157, 159, 160 Extraversion, 70 F Facebook, 24, 139, 145, 147, , 159, 160 Facebook connect, 152 FaceFriend, 62 Face-to-face, 24 Factor analysis, 23, 25, 29, 33, 37 Frequent sharing pattern (frequent spattern), 77 80, 82, 98 D Degree, 260 Degree centrality, 63, 261 Degree centralization, 261 Dendrogram, 256 Density, 260 Springer International Publishing Switzerland 2015 Ö. Ulusoy et al. (eds.), Recommendation and Search in Social Networks, Lecture Notes in Social Networks, DOI / G Generalization, 81, 82 Geodesic distance, 63 Google plus, 140 Gradual trust metric, 43 Group cohesion, 252 Growth rate, 228, 234, 237, 239, 241,

8 288 Index H Hashtag, 1, 2, 6, 8 12, 14 16, 19, 20 Hierarchical clustering, 256, 275 Horizontal style, 24 I Influence, , , 227, 228, , 237, 238, 241, 242, 245, 246 Influence flow, 220, 221, 226, 228, 246 Influence value, 226 Information gain, 12, 17 Internet, 24 Intra-list diversity, 55 Intrinsic value, Italian Parliament, 251 Item novelty, 54 J Jaccard similarity, 121, 128 K Keyboards, 24 Keypads, 24 Keyword adaptation, 1, 4, 7 9 L Last.fm, 119, 121, 123, 130 Live edge, 220, 226 M Markov chains, 44 Maximal frequent sharing pattern (maximal frequent spattern), 77, 79, 98 Membership, 119, 120, 123, 126, 127, 132, 133, 135 Microblog, 2 5, 19 ML-Ask, 25, 26, 29, 37 Modularity, 250, 267, 277 MoleTrust, 43, 49 Multidimensional scaling, 250, 252, 276 Music recommender system, 130 N ndcg, 154, 155, 157 Neighborhood attack, 78, 91, 98 Network value, 217, 219, 221, 222, 228, 232, 234, 237, 241, 242, 246 Neuroticism, 71 Noise ratio, 12 O Openness, 70 Openpolis database, 251 Overlap, 63 P Parliamentarian networks, 259 Partisan coordinate, 253 PCA, 67 Pearson correlation, 12, 120, 121, 123 PerSocialization, 143, 156, 161 Personality, 70 Personality prediction, 72 Personalization, 139, 157, 159, 160 Personalized search, 140, 141 index Personalized searchengine (PER- SOSE), 142 Personalized search engine (PERSOSE), 139, 142, , 160, 161 Precision, , 155 Prediction, 119, 133 Predictive accuracy metrics, 49 51, 55 R Randomization, 81, 82 Recall, Recommendation score, 221, 222 Recommender systems, 41, 48 Rejection sampling, 41, 45, 46, 56 Relevance model, 139, 143, 144, 151, 152, 157, 160, 161 Resolution parameter, 270 S Search engine, 139, 142, 155, 159, 160 Semantic differential method, 25 Seven semesters, , , 266, 267, 270 Seventh semester, 266, 274 Similarity, , 129, 135 Similarity matrix, 253, 258 Simple matching coefficient, 255 Single linkage clustering, 256 Singular value decomposition, 253, 276 Smartphones, 24 Social actions, 139, 144, 145, , , , 160, 162

9 Index 289 Social friendship, 120 Social media question asking (SMQA), 205 Social network, 119, 122, 123, 130, 139, , , Social Network Services (SNS), 24 Social recommender systems, 42 Social tagging, 120, 123, 126 Strongly connected group, 224 Structural Properties, 63 Substantiation, 63 Twitter API, 3, 6, 7, 9 V Vertical style, 24 Viral marketing, , 221, 231, 242, 245 Virtual network, 217, 242 Voting matrices, 252 Voting patterns, 258 T Temporal update, 228 TF-IDF, 5 TidalTrust, 43, 44 Trust aggregation, 42, 44, 49 Trust-based collaborative filtering, 43 Trust-based weighted mean, 44 Tweet, 1 7, 9, 11, Twitter, 24 W Web of trust, 43 Web search, 139, 160 Wikipedia, 139, , 160 Z Zipf law, 45, 48