Social Streams Research Articles

How does organisational culture affect agile projects? A competing values framework perspective

Purpose The literature has not yet delved sufficiently deeper into the holistic relationship between organisational culture and agile project management. This paper aims to address this literature gap by delineating the specifics of why and how organisational culture affects agile project management. Design/methodology/approach This paper addresses this literature gap by conducting a systematic literature review of empirical research investigating how organisational culture affects agile project management. This paper draws on the competing values framework to explain how various dimensions of organisational culture influence the technical and social streams of agile project management. Findings The findings underscored the detrimental effects of hierarchical culture, characterised by rigid organisational structures, formal communication with management and resistance to change acceptance, on both the technical and social aspects of agile projects. In contrast, the positive impact of group culture on the social aspect was evident through employee empowerment and teamwork. Moreover, the rational culture dimension demonstrated favourable effects on both streams, emphasising market knowledge, financial development and business opportunities. Finally, the developmental culture dimension supported customer collaboration, knowledge sharing and innovation. Originality/value The focus on the “mutability” of organisational culture has led to four research propositions delineating specific ways of cultivating organisational culture to be more conducive to agile projects, marking a first in the academic literature.

Interaction graph, topical communities, and efficient local event detection from social streams

Social networks have become an essential part of daily life, and hence every real-world activity finds its place in this virtual world. The present paper proposes a methodology to find localized micro-events from the social network stream. The method is named CommunityINDICATOR. A concept of ‘separation of concerns’ from the software design principle is incorporated in the methodology to reduce the execution time drastically from existing state-of-the-art methods of event detection. In order to reduce the execution time, the algorithm first generates an interaction graph from the social stream and applies community detection followed by a clustering algorithm onto it to detect micro-level events. Experiments have been conducted on Twitter data stream of 5 different cities on three different continents with the size of 2 million tweets. We have used well known quality metrics such as precision, recall, F1-score, accuracy, and execution time to compare performance with other state-of-the-art methodologies. The proposed CommunityINDICATOR provides up to 30% higher accuracy than EvenTweet and SEDTWik in similar execution times. An improvement of 11% to 51% and 17% to 57% in execution time is observed for the proposed algorithm in comparison with TwiiterNews and EventX, respectively, for different datasets.

In Your Eyes: Modality Disentangling for Personality Analysis in Short Video

With the dramatic growth of various short video platforms, users are more likely to share their social stream online and make their social connections stronger. To better understand their preferences, personality analysis has attracted more attention. Unlike single modal data such as text or images, which is hard to comprehensively uncover one's personal traits, personality analysis on short video is verified to be much more accurate but also more challenging because of the huge gap between incompatible data modalities. We have noticed that the key problem is how to disentangle the complexity from multimodal data to find their consistency and uniqueness. In this article, we propose a novel video analysis framework for personality detection with visual, acoustic, and textual neural networks. Specifically, to enhance our model's sensitivity to personality detection, we first propose three deep learning channels to learn modal features. The framework can not only extract each modal feature but also learn time-varying pattern via a temporal alignment network. To identify the consistency and uniqueness across multiple modalities, we creatively propose to maximize the similarity of common information learned by a shared neural network across multiple modalities and extend the distance of exclusive information learned by private networks of different modalities. Extensive experiments on the real-world dataset demonstrate that our model can outperform existing baselines.

DIGITAL TRACES, SOCIAL RESIDUES: USAGE AS SELF-REPRESENTATION ON LIFESTYLE APPS

Self-representation is ubiquitous in digital spaces, but much of the existing literature discussing it is focused on social media contexts. Self-representation involves the creation of media traces that record users’ presence and existence through media, but these traces and the platforms that circulate them do not have to be explicitly or directly social. We study self-representation in alternative digital contexts: lifestyle apps focused on ambient usage behaviors that are generally private, personal, or invisible in online spaces. These apps incorporate social features based on displays of user data—streams repurposing users’ engagement with the platform as social displays to networked friends. The three apps of interest include the payment app Venmo, music streaming app Spotify, and fitness tracking app Strava. We study these apps through a combination of “walkthroughs” of each of the platforms and interviews with users. Our findings suggest that these platforms repurpose individual user behaviors as digital residues in social streams, transforming them into networked self-representations. Within this process, we expect that some users intentionally mobilize their engagement with the apps as social performances. Ultimately, this study raises questions about digital self-making, commodification, and platform sanctioned sociality beyond social media.

Letter from the Editor

Letter from the Editor Dora Malech I’m of the late 20th-century mix-tape generation, an era of cassettes lovingly ordered and bestowed upon friends or more-than-friends with the hope that not only each song but also their chronology and proximity would merit consideration. Curating The Hopkins Review for you elicits a similar pleasure, particularly imagining the physical publication reaching you, a thrill centuries older than my personal nostalgia, complicated and magnified by the sense of responsibility inherent in curating not familiar pop songs but of-the-moment and never-been-read-before literature. If, however, you’re more of a flip-around-the-issue reader, there are no hard feelings here. In fact, may I recommend dipping in on page 25 or page 70 and spending some time with the winners of our inaugural Stephen Dixon Fiction Prize and Anne Frydman Translation Prize, respectively? Of the Dixon Prize-winning story “Gretel” by Hedgie Choi, judge Porochista Khakpour writes, “Hedgie Choi’s ‘Gretel’ manages to be funny, sad, deeply disorienting, and almost mystically magnetic in a way few stories are. . . . I was stunned by how deeply relatable it often was, and . . . by the twists and turns of the plot and Choi’s lush and yet crisp prose that stayed with me long after reading.” Frydman Prize judge Jean McGarry chose Kevin Meehan’s excerpt from Camille’s Lakou, translated from the French and Guadeloupean Kreyol of Marie Léticée’s Moun Lakou (Ibis Rouge Editions, 2016) as her winner, writing of the judging process, “When I ask myself what my criteria might be when reading a piece of translated prose or poetry, these are not very different from what I ask of literature written in English: supple syntax creating an easy and expressive rhythm, diction appropriate to the subject matter and narrative voice, and a linguistic brightness that draws the reader in and proves consistently absorbing. And then, I have to admit one criterion stands out that relates not at all to the quality of the translation: my interest in the work itself.” McGarry quotes the opening sentences of the vignette “Vegetable Soup” (page 74) as an exemplar of such criteria, stating, “A list can be boring, but the odd concreteness of this one enchants.” It’s an honor to share the winning pieces with you in this issue, as it’s also a celebration of Dixon and Frydman’s literary legacy here at Johns Hopkins University and beyond. We were so impressed with the quality of work submitted to the inaugural contests [End Page 1] that we couldn’t resist choosing our own “Editors’ Choice” selections as well, which you can look forward to reading in the next issue, thr 16.2. I’ve long planned that this letter would celebrate our first awards from the journal; it is, however, a more recent surprise that this letter can also celebrate our first award for the journal. At the Modern Language Association of America’s annual mla convention in early January, the Council of Editors of Learned Journals (celj) announced The Hopkins Review as the winner of its Phoenix Award for Significant Editorial or Design Achievement. Of our journal, the judges write, “The Hopkins Review has undertaken a major design overhaul that is visually stunning and inclusive of its locality. . . . The design changes have brought this important arts journal into the social stream of 21st-century cultural connections.” Read the celj judges’ full comments at HopkinsReview.com, along with original web content and more information about submitting your own writing to the next Dixon and Frydman contests. We’re grateful to Dixon and Frydman’s daughters Antonia and Sophia Frydman for their generosity in making these awards possible. Contest submissions (like all submissions) are free for subscribers—a good reason to subscribe, though we believe each piece in this issue is a reason to subscribe, and this year’s surreal exteriors from volume 16’s featured cover artist Se Jong Cho present four more reasons to make sure you receive your quarterly print issue of thr, an award-winning journal of literature and culture with the heart of a mix-tape, and, as always, with . . . Love...

A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams

Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied in order to extract hot and cold spots as polygons on the maps; the more precise the determination of the area of the hot (cold) spots, the greater the computational complexity of the clustering algorithm. Furthermore, these methods do not take into account the hidden information provided by users through social networks, which is significant for detecting the presence of hot/cold spots based on the emotional reactions of citizens. To overcome these critical points, we propose a GIS-based hot and cold spot detection framework encapsulating a classification model of emotion categories of documents extracted from social streams connected to the investigated phenomenon is implemented. The study area is split into subzones; residents’ postings during a predetermined time period are retrieved and analyzed for each subzone. The proposed model measures for each subzone the prevalence of pleasant and unpleasant emotional categories in different time frames; with the aid of a fuzzy-based emotion classification approach, subzones in which unpleasant/pleasant emotions prevail over the analyzed time period are labeled as hot/cold spots. A strength of the proposed framework is to significantly reduce the CPU time of cluster-based hot and cold spot detection methods as it does not require detecting the exact geometric shape of the spot. Our framework was tested to detect hot and cold spots related to citizens’ discomfort due to heatwaves in the study area made up of the municipalities of the northeastern area of the province of Naples (Italy). The results show that the hot spots, where the greatest discomfort is felt, correspond to areas with a high population/building density. On the contrary, cold spots cover urban areas having a lower population density.

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the hybrid extraction and clustering of social events from big social data streams. We rely on a hybrid learning model, where supervised deep learning is used for feature extraction and topic classification, whereas unsupervised spatial clustering is employed to determine the event whereabouts. We present ‘Deep-Eware’, a scalable and efficient event-aware big data platform that integrates data stream and geospatial processing tools for the hybrid extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. The system integrates a semantic keyword generation tool using KeyBERT for dataset preparation. Event classification is performed using CNN and bidirectional LSTM, while hierarchical density-based spatial clustering was used for location-inference of events. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that this hybrid approach for spatio-temporal event extraction has a major advantage for real-time spatio-temporal event detection and tracking from social media. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs.

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the incremental extraction and clustering of social events from big social data streams. We present ‘E-ware’, a scalable and efficient big data platform that integrates data stream and geospatial processing tools for the incremental extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. Our incremental clustering technique employs temporal sliding windows, in order to update the discovered topic clusters with the upcoming social streams (i.e., tweets). The system integrates an efficient spatio-temporal index for fast retrieval and updates of evolving event clusters. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that E-ware has a major advantage for real-time incremental detection and tracking of events, both spatially and temporally. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

Cross event detection and topic evolution analysis in cross events for man-made disasters in social media streams

Cross event detection and topic evolution analysis in cross events for man-made disasters in social media streams

Cross event detection and topic evolution analysis in cross events for man-made disasters in social media streams

Cross event detection and topic evolution analysis in cross events for man-made disasters in social media streams

CrowdE: Filtering Tweets for Direct Customer Engagements

Many consumer brands have customer relationship agents that directly engage opinionated consumers on social streams, such as Twitter. To help agents find opinionated consumers, social stream monitoring tools provide keyword-based filters, which are often too coarse-grained to be effective. In this work, we introduce CrowdE, a Twitter-based filtering system that helps agents find opinionated customers through brand-specific intelligent filters. To minimize per-brand effort in creating these brand-specific filters, the system used a common crowd-enabled process that creates the filters through machine learning over crowd-labeled tweets. We validated the quality of the crowd labels and the performance of the filter algorithms built from the labels. A user evaluation further showed that CrowdE's intelligent filters improved task performance and were generally preferred by users in comparison to keyword-based filters in current social stream monitoring tools.

Workshops

THe 2012 ICWSM conference will hold two workshops: Social Media Visualization, and Real-Time Analysis and Mining of Social Streams.

Preface

The Real-Time Analysis and Mining of Social Streams workshop aims to bring together experts in the real-time analysis and mining of social streams, as well as to further develop and exchange knowledge around these tasks.

Organizers

Organizers of the Real-Time Analysis and Mining of Social Streams workshop.

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion-based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy-based weighted version of the fuzzy c-means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering-based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion-based classes allows the assessment of the classification quality, reporting coherent and consistent results.

COVID-19 Bringing Cairenes Back to their Streets

In a city of high vehicular traffic like Cairo, in some neighborhoods simple social activities were usually practiced in enclosed places. The outbreak of the Covid-19 pandemic has somehow turned the corner in the street life of Cairo as the livability of the urban public spaces enhanced. Residents of Cairo were widely seen using every well-maintained public space they could find. This paper aims to explore the emerging questions concerning Covid-19 and its effect on the city. How did Covid-19 help in bringing residents of Cairo back to the streets? What kind of street activities were introduced during the pandemic and what are the reasons behind them? What are the effects of these novel activities on both the people and the city? Basing on relevant international literature, this research will provide a comparative analysis between urban public life in the streets of Cairo before and during the pandemic. The research adopts a case study qualitative approach for a selected neighborhood in Cairo. It relies on observations, focus group discussions and in-depth interviews with residents from the district. Findings indicate that enclosed commercial and recreational facilities, and vehicular domination in the streets have taken life away from the city before the lockdown period. During the pandemic people have acquired new habits which gave life back to the streets. The flow of the main social stream has a very important role in shaping the street life. As a result of these new circumstances the physical and psychological health of the residents were maintained, new social ties were created and their sense of belonging has been enhanced.

Information Flows Concerning Human Rights Protection in the Digital Representation Structure of Russians’ Civic Engagement in 2020: Results of Social Media Analysis

In the article, the author analysed the structural and content characteristics of the information flow, reflecting the discussion of the problems of protecting the individual’s rights in Russia as a type of civic activity of Russian Internet users. As a result of particular software use, the author accumulated a relevant array of social media document and the dynamics, the modality of the flow, its distribution across various social media, the gender and interpreted age composition of the human rights information audience flow. The author concluded that a sufficiently high potential for protest mobilization of this type of digital stream triggers top-starters of human rights discourse in social media to form non-constructive civic attitudes of Russians. Signs of a significant influence of the human rights stream, first of all, among the younger generation are shown. The author identified methods of using the most resonant events in Russian society’s socio-political life by online network opposition leaders to form unconventional Russians’ attitudes by placing these events in the context of a violation of individual rights. It predetermines a shift in meanings and meanings in the sphere of perception by Russian internet users of the ratio of rights and obligations citizens in the Russian Federation. The necessity of monitoring social and media streams representing the human rights theme in the structure of all streams reflecting the digital civic activity of Russian users is substantiated, the importance of accompanying these streams from the standpoint of creating alternative discourses aimed at overcoming the problem of undermining trust in existing government institutions and contributing to the formation of a culture of constructive civic activity of Russians in digital and offline environments.

Comparison on Understanding of Facebook’s Live Streams According to Shariah Principles for Thai, Malaysian and Indonesian Online Entrepreneurs

The fast growth of digital marketing, social media live stream is one of the provided tools for business competitive such as Facebook, Line, and Instagram. In today’s Islamic business is still very much less presented a clear understanding about Shariah principles. Since adoption trend on Facebook’s live stream has exponentially increased and become the main issue on digital marketing for Muslim communities. This research aims to study and compare a clear understanding of Facebook’s live streaming according to Shariah principal for Thai, Malaysian, and Indonesian. This qualitative research used interview form. We primarily focus on online entrepreneurs based on in-depth interview. Data were collected from 6 key informants from Thailand, Malaysia, and Indonesia. These qualitative data was proposed and analyzed using content analysis. Results indicated that: 1) they are mostly less concern with Shariah principles for online marketing because quickly diffusion on today’s technologies. They are just only to gain high profit of the worldwide market size from national and international targets. 2) the lack of understanding Islamic Shariah as adapted principle, where it is applied to conceptual business model for Facebook’s live stream in practical. As a result, the streamers unaware the relative impact to online consumers. 3) In particular, the streamers have a significant relationship to online consumers. They are mainly tended attracting only towards live stream advertisements. Future study is actively to encourage Islamic organization to build a framework or platform to train a new entrepreneur on digital marketing for Facebook’s live stream. Additionally, it also can provide the reliable information to the best support for their enterprise

Event Popularity Prediction Using Influential Hashtags From Social Media

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach.

Sentiment Analysis for Multi-Attribute Data in OSNs Using Hybrid Approach

Increasing popularity of social networks like LinkedIn, MySpace and other networks in present days. Communication is also increased in between users present in social networks. Large amount of data being move on social media because of increase data outsourcing. Sentiment analysis is impressive and interest concept for online social networks, while different types of existing methods to find sentiment in online social networks to define communication between different users to categorize patterns with respect to similar attributes to analyze large data. We present and suggest the Hybrid Machine Learning method in this paper.(which is combination of Balanced Window and Classification based on Parts of Speech) to handle outsourced data of social networks from Face Book and other blogging services are trained and then classify the relation based on emotional aspect like positive or negative and other relations in social streams. The performance of our proposed approach is to extensively close to machine learning and identify important relevant features randomly and perform sentiment analysis in different data streams. Our experimental results show exhaustive level of classification results with comparison of existing approaches in real time environment.