messages share content about any aspect of their life (public or private), exchanging opinions on a variety of topics and creating and discussing a wide range of events.

This seventh edition of the workshop aims at bringing together academics and practitioners from different areas to promote the vision of social media as social sensors.

Companies like Facebook, Twitter and Instagram have unlocked new ways for people to connect, curate, and consume and also see the surrounding reality. Social Media have changed and continue to change how we interact with the web - how content is distributed, discovered, and delivered. They provide inexpensive communication medium that allows anyone to quickly reach and interact with many other users, modifying their point of view and even their interpretation of the facts. Consequently, in these platforms anyone can publish content and anyone interested in the content can obtain it, representing a transformative revolution in our society. These aspects make social media services the most powerful sensor for any possible interpretation of reality, by also bringing into question the researchers in defining what reality means. The aim of this workshop is to ask researchers to enter such view, by studying how social media can be used in a real-time scenario to detect events and their interpretations.

Nowadays, Social media platforms represent freely-accessible information networks allowing registered (and unregistered) users to read, share and broadcast messages referring to a potentiallyunlimited range of arguments, by also exploiting the immediateness of handy smart devices. This long-running workshop aims at focusing the attention on a particular perspective of these powerful communication channels, which is that of social sensors , where each user reacts in real time to the underlying reality by providing some own interpretation.

Technologies and AI artifacts may support automatic or semiautomatic applications for information detection and integration, offering sideways to the existing authoritative information media and the information reported by the surrounding community.

In light of this, we stress the importance of recognizing this real-time sensor role of Social Media and propose a research venue to discuss about it. We will encourage the submission from both academia and industry. We will seek the participation of both industry and the public sector in the PC Contributions can be submitted as full, short and demo papers, referring to both mature applications and proof of concepts.

• Human-centered computing ; • Information systems ;

social media, sensors, topic detection, data mining

ACM Reference Format:

In the last decade the enormous amount of contents generated by web users created new challenges and new research questions within the data mining community. Here we first report related work about aggregation, recommendation and propagation of information from large scale social networks. We therefore survey the related work on automatic detection of events within usergenerated environments and, finally, we analyze the current state of the art about personalization and user context analysis.

Nowadays, online social media platforms have become the most popular communication system all over the world. In fact, due to the accessibility and the public of these platforms, users tend to shift from traditional communication tools (such as traditional web sites) to these social services. For example, 68 percent of U.S. adults get news on social media in 2018, while in 2012, only 49 percent reported seeing news on social media. Billions of messages, in many different formats, are appearing daily in these services such as Twitter, Instagram, Facebook, etc. The authors of these

A first issue when dealing with heterogeneous data sources is the aggregation of the content through filtering and merging techniques. Considering Social Networks as a source of text data, many web services like TweetTabs¹ aggregate messages and links through user-friendly interfaces. In general, clustering techniques help in finding groups of similar contents that can be further filtered using labeling techniques [Treeratpituk and Callan 2006].

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1http://tweettabs.com

https://doi.org/10.1145/3511095.3532573

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While these services simply aggregate messages and/or links, one of the most explored tasks in mining of text entries streams from social media is the recommendation of topics, URLs, friends, and so forth. So far, two main high-level approaches have been studied: collaborative filtering and content-based techniques. While the first aims at selecting and proposing content by looking at what similar users have already selected (as in [Goldberg et al. 1992]), the second analyzes the semantics of the content without considering its origin (as in [Hassan et al. 2009]). More recently, hybrid approaches have been also proposed by [Balabanovic and Shoham 1997; Melville et al. 2001].

Recommendation systems can differ on what they recommend: URLs (as in [Chen et al. 2010]), users that share same interests (as in [Chen et al. 2009]) and tags in folksonomy-based systems (like the approach proposed in [Jäschke et al. 2007]).

Another issue when dealing with huge, time-sensitive, and usergenerated text content is the analysis of how such information spreads through the blogosphere or a social network. Generally speaking, research on flows of information in networks initially started from the analogy with the spread of a disease in a social environment. This model is based on the following disease life cycle: someone is first susceptible to the disease and then, if exposed to the disease by an infectious contact, he becomes infected (and infectious) with some probability. In general, there exist two main approaches to model propagation, namely threshold models [Granovetter 1978] and cascade models [Goldenberg et al. 2001]. While the first one treats the problem as a chain-reaction of influence where each node in the network obeys to a monotone activation function, in the second one nodes close to each others have some chance to influence each others as well.

In [Gruhl et al. 2004], the authors studied the dynamics of web environments at topic- and user-level, inducing propagation networks from a sequence of posts in the blogosphere. [Goyal et al. 2010] studied how to learn influence probabilities from a log of past propagations in the Flickr² community. [Yang and Leskovec 2010] developed the Linear Influence model, where the influence functions of individual nodes govern the overall rate of diffusion through the network. In [Cha et al. 2010] the authors present a comparison between three measures of influence: in-degree, retweets and mentions in Twitter, investigating the dynamics of users’ influence across topics and time. Still, [Yang and Counts 2010] proposed a model to capture properties of information diffusion like speed, scale, and range.

Focusing on Twitter-based approaches, Twopular³ represents an example from which it is possible to analyze trends along a timeline. In general, several studies examined topics and their changes across time in dynamic text corpora. The general approach orders and clusters the documents according to the timestamps, analyzing the relative distributions (see also [Griffiths and Steyvers 2004]). [Zhao et al. 2007] represent social text streams as multi-graphs, where each node represents a social actor and each edge represents the information flow between two actors. In [Favenza et al. 2008; Qi and Candan 2006], the authors present a system which uses curve analysis of frequencies for automatic segmentation of topics.

2http://www.flickr.com

3http://twopular.com

[Wu et al. 2010] use the tolerance rough set model to enrich the set of feature words into an approximated latent semantic space, from which they extract hot topics by a complete-link clustering. [Abrol and Khan 2010] analyze tweets in order to predict whether the user is looking for news or not, and determine keywords that can be added to her web search query. [Asur et al. 2011] explore the longevity of trending topics on Twitter, and analyze the role of users in the emergence of trends. The role of the users, their collaboration and the influence among them has been also extensively studied in social networks [Katz et al. 1997], from qualitative studies on cooperation behaviors [Chubin 1976; Crane 1969; Shapin 1981] to more quantitative approaches [de Beaver and Rosen 1979; Melin and Persson 1996]. The latter includes collaboration networkbased studies (because of a social network can be easily seen as a social network where people form teams to produce some results), which are generally aiming at understanding the structural determinants and patterns of collaboration [Barabasi et al. 2002; Di Caro et al. 2012; Newman 2001; Schifanella et al. 2012]. Such networks have been deeply analyzed by looking at properties like network topology, size and evolution [Hou et al. 2008; Moon et al. 2010].

[Chen et al. 2003] were the first to present an aging theory based on a biological metaphor. Using this approach, the work presented by [Wang et al. 2008] is able to rank topics from online news streams through the concept of burstiness . The burstiness of a term, already introduced by [He et al. 2007], is computed with a χ -statistic on its temporal contingency table. Although this work shares our goal, it is based on the concepts of user attention and the media focus, whereas our proposed approach is independent from them and it is assumed to be less complex and more general.

Identifying events in real time on Social Networks is a challenging problem, due to the heterogeneity and immense scale of the data. In fact, as already reported in the Introduction, users post messages with a variety of purposes. For this, while many contents are not specifically related to any particular real-world event, informative event messages nevertheless abound.

Regarding e.g. the classification of single tweets, [Sriram et al. 2010] define a typology of five generic classes of tweets (news, events, opinions, deals, and private messages) in order to improve information filtering and recognize events. The research works proposed in [Becker et al. 2010, 2011; Sankaranarayanan et al. 2009] focused on identifying events in social media in general, and on Twitter. Recent works on this social network started to process data as a stream with the goal of identifying events of a particular type (e.g., news events , earthquakes, etc). [Petrović et al. 2010]) identify the first Twitter message associated with an event in order to analyze the starting point of the related information flow.

The real-time social content can also be seen as a sensor that captures what is happening in the world: similarly to the recommendation task, this can be exploited for a zero-delay information broadcasting system that detects emerging concepts. Generally, all the techniques rely on some measure of importance of the keywords. [Bun et al. 2002] present the TF ∗ PDF algorithm which extends the well-known TF − IDF to avoid the collapse of important terms when they appear in many text documents. Indeed, the

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IDF component decreases the frequency value for a keyword when it is frequently used. Considering different newswire sources or channels, the weight of a term from a single channel is linearly proportional to the term’s frequency within it, while it is exponentially proportional to the ratio of documents that contain the term in the channel itself. [Lampos and Cristianini 2012] presented a supervised learning system for the extraction of events from unstructured textual information that relies on geo-tagged Twitter posts. [Takeshi Sakaki and Matsuo 2010] consider Twitter as a social sensor for detecting large scale events like earthquakes, typhoons and traffic jams. The authors analyze the context of such keyword in order to discriminate them as positive or negative (the sentence “Someone is shaking hands with my boss” should be captured as negative even though it contains the term “shake”). In [AlSumait et al. 2008] the authors present a system called OLDA (Online Topic Model) which permits to automatically capture the thematic patterns and identifies emerging topics of text streams and their changes over time.

Since our Workshop aims at including the topic of personalization of emerging topics, we also cover here the relevant works that have been done in this field. Most of the literature refers to this task as “personalization of search results”, or “user-driven personalization” (as in the works proposed in [Noll and Meinel 2007; Sugiyama et al. 2004; Teevan et al. 2005b; Ziegler et al. 2005]). As stated by [Teevan et al. 2005a], the motivation behind the interest around this area of research is based on the reasonable assumption that different users generally expect different information even with the same query. There obviously exist several approaches of facing such a task. For instance, depending on the domain, one may be interested in re-ranking the results based on their relevance [Noll and Meinel 2007; Teevan et al. 2005b], rather then diversify or visualize them (as in [Agrawal et al. 2009; Candan et al. 2012; Di Caro et al. 2011; Lin et al. 2010; Radlinski and Dumais 2006; Wedig and Madani 2006]). Still, the actual personalization and visualization of contents (whether they are search results or emerging topics as in our case) could be made by leveraging different kinds of data: users’ contents [Cantador et al. 2010; Xu et al. 2008], ontologies [Gauch et al. 2003; Han et al. 2010; Sieg et al. 2007], and users’s social network and activity [Carmel et al. 2009; Wang and Jin 2010]. Given this brief overview, our system can be classified as a re-ranking approach that makes use of the users’ contents, i.e., the tweets posted by them. In addition to this simple scheme, we also wanted to take into account the temporal aspect associated to the tweets in order to weight more what has been recently posted, and the other way around.

We would like to thank the organizing committee of HT2022 for giving us the opportunity to organize the workshop. Second, we would like to thank our program committee members. And of course, we would like to thank all the authors of the workshop for submitting their research works and for their participation.

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