Pub Date : 2023-09-26DOI: 10.1140/epjds/s13688-023-00403-8
Audun Myers, David Muñoz, Firas A. Khasawneh, Elizabeth Munch
{"title":"Correction: Temporal network analysis using zigzag persistence","authors":"Audun Myers, David Muñoz, Firas A. Khasawneh, Elizabeth Munch","doi":"10.1140/epjds/s13688-023-00403-8","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00403-8","url":null,"abstract":"","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134887148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.1140/epjds/s13688-023-00406-5
Abdullah Alrhmoun, János Kertész
Abstract Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors.
{"title":"Emergent local structures in an ecosystem of social bots and humans on Twitter","authors":"Abdullah Alrhmoun, János Kertész","doi":"10.1140/epjds/s13688-023-00406-5","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00406-5","url":null,"abstract":"Abstract Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136062242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1140/epjds/s13688-023-00400-x
Peter Sheridan Dodds, Joshua R. Minot, Michael V. Arnold, Thayer Alshaabi, Jane Lydia Adams, David Rushing Dewhurst, Tyler J. Gray, Morgan R. Frank, Andrew J. Reagan, Christopher M. Danforth
Abstract Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.
{"title":"Allotaxonometry and rank-turbulence divergence: a universal instrument for comparing complex systems","authors":"Peter Sheridan Dodds, Joshua R. Minot, Michael V. Arnold, Thayer Alshaabi, Jane Lydia Adams, David Rushing Dewhurst, Tyler J. Gray, Morgan R. Frank, Andrew J. Reagan, Christopher M. Danforth","doi":"10.1140/epjds/s13688-023-00400-x","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00400-x","url":null,"abstract":"Abstract Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135014878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The aim of this paper is to analyze the Arab peoples reactions and attitudes towards the Russo-Ukraine War through the social media of posted tweets, as a fast means to express opinions. We scrapped over 3 million tweets using some keywords that are related to the war and performed sentiment, emotion, and partiality analyses. For sentiment analysis, we employed a voting technique of several pre-trained Arabic language foundational models. For emotion analysis, we utilized a pre-constructed emotion lexicon. The partiality is analyzed through classifying tweets as being ‘Pro-Russia’, ‘Pro-Ukraine’, or ‘Neither’; and it indicates the bias or empathy towards either of the conflicting parties. This was achieved by constructing a weighted lexicon of n-grams related to either side. We found that the majority of the tweets carried ‘Negative’ sentiment. Emotions were not that obvious with a lot of tweets carrying ‘Mixed Feelings’. The more decisive tweets conveyed either ‘Joy’ or ‘Anger’ emotions. This may be attributed to celebrating victory (‘Joy’) or complaining from destruction (‘Anger’). Finally, for partiality analysis, the amount of tweets classified as being ‘Pro-Ukraine’ was slightly greater than Pro-Russia’ at the beginning of the war (specifically from Feb 2022 till April 2022) then slowly began to decrease until they nearly converged at the start of June 2022 with a shift happening in the empathy towards Russia in August 2022. Our Interpretation for that is with the initial Russian fierce and surprise attack at the beginning and the amount of refugees who escaped to neighboring countries, Ukraine gained much empathy. However, by April 2022, Russian intensity has been decreased and with heavy sanctions the U.S. and West have applied on Russia, Russia has begun to gain such empathy with decrease on the Ukrainian side.
{"title":"Arab reactions towards Russo-Ukrainian war","authors":"Moayadeldin Tamer, Mohamed A. Khamis, Abdallah Yahia, SeifALdin Khaled, Abdelrahman Ashraf, Walid Gomaa","doi":"10.1140/epjds/s13688-023-00415-4","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00415-4","url":null,"abstract":"Abstract The aim of this paper is to analyze the Arab peoples reactions and attitudes towards the Russo-Ukraine War through the social media of posted tweets, as a fast means to express opinions. We scrapped over 3 million tweets using some keywords that are related to the war and performed sentiment, emotion, and partiality analyses. For sentiment analysis, we employed a voting technique of several pre-trained Arabic language foundational models. For emotion analysis, we utilized a pre-constructed emotion lexicon. The partiality is analyzed through classifying tweets as being ‘Pro-Russia’, ‘Pro-Ukraine’, or ‘Neither’; and it indicates the bias or empathy towards either of the conflicting parties. This was achieved by constructing a weighted lexicon of n-grams related to either side. We found that the majority of the tweets carried ‘Negative’ sentiment. Emotions were not that obvious with a lot of tweets carrying ‘Mixed Feelings’. The more decisive tweets conveyed either ‘Joy’ or ‘Anger’ emotions. This may be attributed to celebrating victory (‘Joy’) or complaining from destruction (‘Anger’). Finally, for partiality analysis, the amount of tweets classified as being ‘Pro-Ukraine’ was slightly greater than Pro-Russia’ at the beginning of the war (specifically from Feb 2022 till April 2022) then slowly began to decrease until they nearly converged at the start of June 2022 with a shift happening in the empathy towards Russia in August 2022. Our Interpretation for that is with the initial Russian fierce and surprise attack at the beginning and the amount of refugees who escaped to neighboring countries, Ukraine gained much empathy. However, by April 2022, Russian intensity has been decreased and with heavy sanctions the U.S. and West have applied on Russia, Russia has begun to gain such empathy with decrease on the Ukrainian side.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135396505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-12DOI: 10.1140/epjds/s13688-023-00414-5
Dominique Geissler, Dominik Bär, Nicolas Pröllochs, Stefan Feuerriegel
Abstract The Russian invasion of Ukraine in February 2022 was accompanied by practices of information warfare, yet existing evidence is largely anecdotal while large-scale empirical evidence is lacking. Here, we analyze the spread of pro-Russian support on social media. For this, we collected $N = 349{,}455$ N=349,455 messages from Twitter with pro-Russian support. Our findings suggest that pro-Russian messages received ∼251,000 retweets and thereby reached around 14.4 million users. We further provide evidence that bots played a disproportionate role in the dissemination of pro-Russian messages and amplified its proliferation in early-stage diffusion. Countries that abstained from voting on the United Nations Resolution ES-11/1 such as India, South Africa, and Pakistan showed pronounced activity of bots. Overall, 20.28% of the spreaders are classified as bots, most of which were created at the beginning of the invasion. Together, our findings suggest the presence of a large-scale Russian propaganda campaign on social media and highlight the new threats to society that originate from it. Our results also suggest that curbing bots may be an effective strategy to mitigate such campaigns.
俄罗斯2022年2月入侵乌克兰伴随着信息战的实践，但现有证据大多是轶事，缺乏大规模的经验证据。在这里，我们分析亲俄支持在社交媒体上的传播。为此，我们从支持亲俄的Twitter上收集了$N = 349{，}455$ N = 349, 455条消息。我们的研究结果表明，亲俄信息获得了约251,000次转发，从而达到了约1440万用户。我们进一步提供证据表明，机器人在亲俄信息的传播中发挥了不成比例的作用，并在传播的早期阶段扩大了其扩散。对联合国ES-11/1号决议投弃权票的国家，如印度、南非、巴基斯坦等，显示出明显的机器人活动。总体而言，20.28%的传播器被归类为机器人，其中大多数是在入侵开始时创建的。总之，我们的研究结果表明，俄罗斯在社交媒体上开展了大规模的宣传活动，并强调了由此产生的对社会的新威胁。我们的研究结果还表明，遏制机器人可能是减轻此类活动的有效策略。
{"title":"Russian propaganda on social media during the 2022 invasion of Ukraine","authors":"Dominique Geissler, Dominik Bär, Nicolas Pröllochs, Stefan Feuerriegel","doi":"10.1140/epjds/s13688-023-00414-5","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00414-5","url":null,"abstract":"Abstract The Russian invasion of Ukraine in February 2022 was accompanied by practices of information warfare, yet existing evidence is largely anecdotal while large-scale empirical evidence is lacking. Here, we analyze the spread of pro-Russian support on social media. For this, we collected $N = 349{,}455$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>349</mml:mn> <mml:mo>,</mml:mo> <mml:mn>455</mml:mn> </mml:math> messages from Twitter with pro-Russian support. Our findings suggest that pro-Russian messages received ∼251,000 retweets and thereby reached around 14.4 million users. We further provide evidence that bots played a disproportionate role in the dissemination of pro-Russian messages and amplified its proliferation in early-stage diffusion. Countries that abstained from voting on the United Nations Resolution ES-11/1 such as India, South Africa, and Pakistan showed pronounced activity of bots. Overall, 20.28% of the spreaders are classified as bots, most of which were created at the beginning of the invasion. Together, our findings suggest the presence of a large-scale Russian propaganda campaign on social media and highlight the new threats to society that originate from it. Our results also suggest that curbing bots may be an effective strategy to mitigate such campaigns.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135786434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers.
{"title":"DWAEF: a deep weighted average ensemble framework harnessing novel indicators for sarcasm detection1","authors":"Richa Sharma, Simrat Deol, Udit Kaushish, Prakher Pandey, Vishal Maurya","doi":"10.3233/ds-220058","DOIUrl":"https://doi.org/10.3233/ds-220058","url":null,"abstract":"Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"63 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87742058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-23DOI: 10.1140/epjds/s13688-023-00410-9
Alexander C. Nwala, A. Flammini, F. Menczer
{"title":"A language framework for modeling social media account behavior","authors":"Alexander C. Nwala, A. Flammini, F. Menczer","doi":"10.1140/epjds/s13688-023-00410-9","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00410-9","url":null,"abstract":"","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"12 1","pages":"1-22"},"PeriodicalIF":3.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47164460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-23DOI: 10.1140/epjds/s13688-023-00404-7
Kunwoo Park, Jungseock Joo
{"title":"Perceived masculinity from Facebook photographs of candidates predicts electoral success","authors":"Kunwoo Park, Jungseock Joo","doi":"10.1140/epjds/s13688-023-00404-7","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00404-7","url":null,"abstract":"","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":" ","pages":"1-20"},"PeriodicalIF":3.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49372888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-16DOI: 10.1140/epjds/s13688-023-00408-3
A. Dmowska, T. Stepinski
{"title":"Spatio-temporal changes in racial segregation and diversity in large US cities from 1990 to 2020: a visual data analysis","authors":"A. Dmowska, T. Stepinski","doi":"10.1140/epjds/s13688-023-00408-3","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00408-3","url":null,"abstract":"","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"12 1","pages":"1-18"},"PeriodicalIF":3.6,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49364227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.1140/epjds/s13688-023-00393-7
Joshua Garland, Keyan Ghazi-Zahedi, Jean-Gabriel Young, Laurent Hébert-Dufresne, M. Galesic
{"title":"Correction: Impact and dynamics of hate and counter speech online","authors":"Joshua Garland, Keyan Ghazi-Zahedi, Jean-Gabriel Young, Laurent Hébert-Dufresne, M. Galesic","doi":"10.1140/epjds/s13688-023-00393-7","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00393-7","url":null,"abstract":"","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"12 1","pages":"1"},"PeriodicalIF":3.6,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44515215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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